diff --git a/harness/run_submission.py b/harness/run_submission.py index fbea6d2..5b9e1b2 100755 --- a/harness/run_submission.py +++ b/harness/run_submission.py @@ -103,7 +103,7 @@ def main(): print(f"\n [harness] Run {run+1} of {num_runs}") # 4. Client-side: Generate a new random input using harness/generate_input.py - cmd_args = [str(size),] + cmd_args = [str(size), "--dataset", dataset_name] if seed is not None: # Use a different seed for each run but derived from the base seed rng = np.random.default_rng(seed) diff --git a/harness/utils.py b/harness/utils.py index e63fada..3edd1a3 100644 --- a/harness/utils.py +++ b/harness/utils.py @@ -50,7 +50,7 @@ def parse_submission_arguments(workload: str) -> Tuple[int, InstanceParams, int, help='Specify with 1 if to rerun the cleartext computation') parser.add_argument('--remote', action='store_true', help='Run example submission in remote backend mode') - parser.add_argument('--model', default='mlp', type=str, + parser.add_argument('--model', default=None, type=str, help='Pick a model run (default: mlp)') parser.add_argument('--dataset', default='mnist', type=str, help='Pick a dataset run (default: mnist)') @@ -64,9 +64,20 @@ def parse_submission_arguments(workload: str) -> Tuple[int, InstanceParams, int, remote_be = args.remote # adding model and dataset to the arguments - model_name = args.model.lower() dataset_name = args.dataset.lower() + # Dataset-specific model defaults + dataset_model_defaults = { + "mnist": "mlp", + "cifar10": "resnet20", + } + + # Model selection: use provided model or dataset-specific default + if args.model is None: + model_name = dataset_model_defaults.get(dataset_name, "mlp") + else: + model_name = args.model.lower() + # Use params.py to get instance parameters params = InstanceParams(size) return size, params, seed, num_runs, clrtxt, remote_be, model_name, dataset_name @@ -81,17 +92,17 @@ def ensure_directories(rootdir: Path): f"not found in {rootdir}") sys.exit(1) -def build_submission(script_dir: Path, model_name: str, remote_be: bool): +def build_submission(script_dir: Path, dataset_name: str, remote_be: bool): """ Build the submission, including pulling dependencies as neeed """ if remote_be: - subprocess.run(["pip", "install", "-r", f"./submission_remote/{model_name}/requirements.txt"], check=True) + subprocess.run(["pip", "install", "-r", f"./submission_remote/{dataset_name}/requirements.txt"], check=True) else: # Clone and build OpenFHE if needed subprocess.run([script_dir/"get_openfhe.sh"], check=True) # CMake build of the submission itself - subprocess.run([script_dir/"build_task.sh", f"./submissions/{model_name}"], check=True) + subprocess.run([script_dir/"build_task.sh", f"./submissions/{dataset_name}"], check=True) class TextFormat: BOLD = "\033[1m" diff --git a/submission_remote/mnist/__pycache__/submission_utils.cpython-312.pyc b/submission_remote/mnist/__pycache__/submission_utils.cpython-312.pyc new file mode 100644 index 0000000..f88bcb4 Binary files /dev/null and b/submission_remote/mnist/__pycache__/submission_utils.cpython-312.pyc differ diff --git a/submissions/cifar10/CMakeLists.txt b/submissions/cifar10/CMakeLists.txt new file mode 100644 index 0000000..4c94232 --- /dev/null +++ b/submissions/cifar10/CMakeLists.txt @@ -0,0 +1,115 @@ +cmake_minimum_required(VERSION 3.14) +project(add_resnet20 LANGUAGES CXX) + +# --------------------------------------------------------------- +# 1. Compiler / flags +# -------------------------------------------------------------------- +set(CMAKE_CXX_STANDARD 17) +# set(CMAKE_POSITION_INDEPENDENT_CODE ON) +# add_compile_options(-O3 -Wall -Wextra -Wno-unused-parameter) +option( BUILD_STATIC "Set to ON to include static versions of the library" OFF) + +# -------------------------------------------------------------------- +# 2. Find the OpenFHE *package* that the install script put into +# third_party/openfhe +# -------------------------------------------------------------------- +# scripts/get_openfhe.sh installs: +# third_party/openfhe/lib/cmake/OpenFHE/OpenFHEConfig.cmake + +find_package(OpenFHE CONFIG REQUIRED) +if (OpenFHE_FOUND) + message(STATUS "FOUND PACKAGE OpenFHE") + message(STATUS "OpenFHE Version: ${BASE_OPENFHE_VERSION}") + message(STATUS "OpenFHE installed as shared libraries: ${OpenFHE_SHARED}") + message(STATUS "OpenFHE include files location: ${OpenFHE_INCLUDE}") + message(STATUS "OpenFHE lib files location: ${OpenFHE_LIBDIR}") + message(STATUS "OpenFHE Native Backend size: ${OpenFHE_NATIVE_SIZE}") +else() + message(FATAL_ERROR "PACKAGE OpenFHE NOT FOUND") +endif () + +set( CMAKE_CXX_FLAGS "${OpenFHE_CXX_FLAGS} -Werror") + +# -------------------------------------------------------------------- +# 3. Link libraries +# -------------------------------------------------------------------- + +include_directories( ${OPENMP_INCLUDES} ) +include_directories( ${OpenFHE_INCLUDE} ) +include_directories( ${OpenFHE_INCLUDE}/third-party/include ) +include_directories( ${OpenFHE_INCLUDE}/core ) +include_directories( ${OpenFHE_INCLUDE}/pke ) +include_directories( ${OpenFHE_INCLUDE}/binfhe ) +### add directories for other OpenFHE modules as needed for your project + +include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include) +include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include/fheon) + +link_directories( ${OpenFHE_LIBDIR} ) +link_directories( ${OPENMP_LIBRARIES} ) + +# set(CMAKE_SKIP_BUILD_RPATH FALSE) +# set(CMAKE_BUILD_WITH_INSTALL_RPATH OFF) +# set(CMAKE_BUILD_RPATH "${OpenFHE_LIBDIR}") +# set(CMAKE_INSTALL_RPATH "${OpenFHE_LIBDIR}") +# set(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE) +add_link_options(-Wl,--no-as-needed) + +# These should take care of setting the correct LD path without manually setting LD_LIBRARY_PATH. +# But if a shared library or symbol lookup error occurs, you need to set the LD_LIBRARY_PATH environment +# to the OpenFHE lib directory: export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/path/to/OpenFHE/lib + +if(BUILD_STATIC) + set( CMAKE_EXE_LINKER_FLAGS "${OpenFHE_EXE_LINKER_FLAGS} -static") + link_libraries( ${OpenFHE_STATIC_LIBRARIES} ) + message(STATUS "Build with static libs") +else() + set( CMAKE_EXE_LINKER_FLAGS ${OpenFHE_EXE_LINKER_FLAGS} ) + link_libraries( ${OpenFHE_SHARED_LIBRARIES} ) +endif() + +# -------------------------------------------------------------------- +# 4. Create mlp library +# -------------------------------------------------------------------- +add_library( fheonhecontroller ../fheon/FHEONHEController.cpp ) +target_include_directories( fheonhecontroller PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include/fheon ) +add_library( fheonanncontroller ../fheon/FHEONANNController.cpp ) +target_include_directories( fheonanncontroller PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include/fheon ) + +#----------------------------------------------------------------------- +# Create the FHEON Libraries +#------------------------------------------------------------------------ +add_library( encryption_utils src/encryption_utils.cpp ) +target_include_directories( encryption_utils PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/include ) + +# Use pre-built mlp_openfhe library +add_library( mlp_openfhe STATIC IMPORTED ) +set_target_properties( mlp_openfhe PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_SOURCE_DIR}/../pre-built-library/libmlp_openfhe.a ) + +# -------------------------------------------------------------------- +# 5. Each *.cpp file becomes its own executable. +# The seven stage names are hard-wired by the benchmark contract. +# -------------------------------------------------------------------- + +add_executable( client_key_generation src/client_key_generation.cpp ) +target_link_libraries( client_key_generation fheonanncontroller ) +target_link_libraries( client_key_generation fheonhecontroller ) + +add_executable( client_preprocess_input src/client_preprocess_input.cpp ) + +add_executable( client_encode_encrypt_input src/client_encode_encrypt_input.cpp ) +target_link_libraries( client_encode_encrypt_input encryption_utils ) + +add_executable( client_decrypt_decode src/client_decrypt_decode.cpp ) +target_link_libraries( client_decrypt_decode encryption_utils ) + +add_executable( client_postprocess src/client_postprocess.cpp ) + +add_executable( server_preprocess_model src/server_preprocess_model.cpp ) + +add_executable( server_encrypted_compute src/server_encrypted_compute.cpp src/resnet20_fheon.cpp ) +target_link_libraries( server_encrypted_compute mlp_openfhe) +target_link_libraries( server_encrypted_compute encryption_utils ) +target_link_libraries( server_encrypted_compute fheonhecontroller ) +target_link_libraries( server_encrypted_compute fheonanncontroller ) +target_compile_definitions(server_encrypted_compute PRIVATE WEIGHTS_DIR="${CMAKE_CURRENT_SOURCE_DIR}/weights/resnet20/") diff --git a/submissions/cifar10/docs/README.md b/submissions/cifar10/docs/README.md new file mode 100644 index 0000000..fd1e136 --- /dev/null +++ b/submissions/cifar10/docs/README.md @@ -0,0 +1,47 @@ +# ResNet-20: Privacy-Preserving Encrypted Inference + +This submission implements a ResNet-20 deep residual network using the FHEON framework for privacy-preserving machine learning inference on CIFAR-10 images. + +## Overview + +ResNet-20 is a deep residual network designed to classify 3×32×32 RGB images from the CIFAR-10 dataset using Fully Homomorphic Encryption (FHE). This implementation uses the FHEON framework built on top of OpenFHE. + +## FHEON Framework + +**FHEON** is a configurable framework designed to facilitate the implementation of privacy-preserving neural network inference using FHE. Built on top of OpenFHE, FHEON provides high-level abstractions for common deep learning components while optimizing for the unique constraints of homomorphic computation. + +For more information, visit the [official website](https://fheon.pqcsecure.org/), explore the [source code](https://github.com/stamcenter/fheon), or read the [research paper](https://arxiv.org/abs/2510.03996). + +## Model Architecture: ResNet-20 + +A deep residual network targeting CIFAR-10 built using FHEON. + +- **Architecture**: Initial convolution, three stages of ResNet blocks with shortcuts, and Global Average Pooling. +- **Dataset**: CIFAR-10 +- **Bootstrapping**: Strategic integration of CKKS bootstrapping to maintain circuit depth. +- **Implementation**: `submissions/resnet20/src/resnet20_fheon.cpp` + +--- + +## Security Level + +ResNet-20 is configured to satisfy the **128-bit security level** using the standardized parameters for CKKS as defined in the [Homomorphic Encryption Standard v1.1](https://homomorphicencryption.org/wp-content/uploads/2018/11/HomomorphicEncryptionStandardv1.1.pdf). + +### CKKS Parameters +- **Ciphertexts depth**: 29 +- **log PQ**: 708 +- **Cyclotomic Order**: 131072 +- **Ring dimension**: 65536 +- **Number of Slots**: 32768 + + +## Performance Optimization + +The `client_key_generation` utilities provide equivalent ring dimensions and slot counts that target smaller security levels. These configurations are designed to significantly improve computation speed and reduce memory overhead, consistent with the performance benchmarks presented in the FHEON research paper. + +--- + +### Execution Paths +The inference executables typically expect external weights and keys provided at runtime: +- **Weights**: `submissions//weights/` +- **Keys**: Generated and managed via model-specific `client_key_generation` utilities. \ No newline at end of file diff --git a/submissions/cifar10/fheon/FHEONANNController.cpp b/submissions/cifar10/fheon/FHEONANNController.cpp new file mode 100755 index 0000000..8bfe0bc --- /dev/null +++ b/submissions/cifar10/fheon/FHEONANNController.cpp @@ -0,0 +1,2144 @@ + +/*********************************************************************************************************************** +* +* @author: Nges Brian, Njungle +* +* MIT License +* Copyright (c) 2025 Secure, Trusted and Assured Microelectronics, Arizona State University + +* Permission is hereby granted, free of charge, to any person obtaining a copy +* of this software and associated documentation files (the "Software"), to deal +* in the Software without restriction, including without limitation the rights +* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +* copies of the Software, and to permit persons to whom the Software is +* furnished to do so, subject to the following conditions: + +* The above copyright notice and this permission notice shall be included in all +* copies or substantial portions of the Software. + +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +********************************************************************************************************************/ + +/** + * @brief FHE Controller for defining and managing homomorphic ANN functions. + * + * This class provides different methods for convolution, pooling, fully connected + * relu, etc for neural network development on encrypted data using FHE. + */ + +#include +#include +#include +#include +#include +#include "FHEONANNController.h" + +namespace fs = std::filesystem; + +void FHEONANNController::setContext(CryptoContext& in_context){ + context = in_context; +} + +/** + * @brief Generate the rotation positions required for convolution layers in homomorphic encryption. + * + * This function computes the rotation positions needed for performing convolutions + * on encrypted data. It first calculates the output shape of the convolution layer + * given the input image dimensions, kernel parameters, padding, and stride, and + * then derives the set of rotation positions based on the output width. + * + * @param inputWidth Width of the input image (assuming square images). + * @param inputChannels Number of input channels to the convolution layer. + * @param outputChannels Number of output channels in the convolution layer. + * @param kernelWidth Size of the convolution kernel (assumed square). + * @param padding Amount of zero-padding applied around the input image. + * @param stride stride length used for the convolution. + * + * @return A vector of integers representing the rotation positions required + * to perform the convolution. + */ +vector FHEONANNController::generate_convolution_rotation_positions(int inputWidth, int inputChannels, int outputChannels, + int kernelWidth, int padding, int stride){ + + vector keys_position; + int inputWidth_sq = pow(inputWidth, 2); + int padded_width = inputWidth+(2*padding); + int padding_width_sq = pow(padded_width, 2); + int width_out = ((padded_width - (kernelWidth - 1) - 1)/stride)+1; + int width_out_sq = pow(width_out,2); + keys_position.push_back(inputWidth); + keys_position.push_back(padded_width); + keys_position.push_back(padding_width_sq); + keys_position.push_back(inputWidth_sq); + keys_position.push_back(width_out); + keys_position.push_back(width_out_sq); + keys_position.push_back(-1); + keys_position.push_back(1); + int rot_val; + + /** Convolution rotations */ + for(int i=1; i < kernelWidth;i++){ + keys_position.push_back(i); + } + + for(int i=1; i FHEONANNController::generate_avgpool_rotation_positions(int inputWidth, int kernelWidth, + int stride, int inputChannels){ + + vector keys_position; + int width_avgpool_out = (inputWidth/stride); + int width_avgpool_sq = pow(width_avgpool_out, 2); + int width_sq = pow(inputWidth, 2); + keys_position.push_back(width_sq); + keys_position.push_back(inputWidth); + keys_position.push_back(kernelWidth); + keys_position.push_back(stride); + keys_position.push_back(width_avgpool_out); + keys_position.push_back((stride*inputWidth)); + + for(int i=1; i FHEONANNController::generate_optimized_convolution_rotation_positions( + int inputWidth, int inputChannels, int outputChannels, int stride, string stridingType){ + + vector keys_position; + int inputWidth_sq = pow(inputWidth, 2); + int width_out = (inputWidth/stride); + int width_out_sq = pow(width_out,2); + keys_position.push_back(-1); + keys_position.push_back(1); + keys_position.push_back(inputWidth_sq); + keys_position.push_back(inputWidth); + keys_position.push_back(-inputWidth); + + if(stride > 1){ + if(stridingType == "basic"){ + /**** USE THIS BLOCK FOR BASIC STRIDING */ + for(int i=1; i < inputChannels; i++){ + int rot_val = i*width_out; + keys_position.push_back(-rot_val); + + rot_val = i*width_out_sq; + keys_position.push_back(-rot_val); + } + for(int i =1; i< width_out; i++){ + int rot_val = i*width_out; + keys_position.push_back(i); + keys_position.push_back(-rot_val); + } + } + else if(stridingType == "single_channel"){ + for (int s=1; s FHEONANNController::generate_avgpool_optimized_rotation_positions(int inputWidth, int inputChannels, + int kernelWidth, int stride, bool globalPooling, string stridingType, int rotationIndex){ + + vector keys_position; + if(globalPooling){ + keys_position.push_back((inputWidth*inputWidth)); + keys_position.push_back(-inputChannels); + for(int pos=0; pos1){ + if(stridingType == "basic"){ + /**** USE THIS BLOCK FOR BASIC STRIDING */ + for(int i=1; i < inputChannels; i++){ + int rot_val = i*width_avgpool_out; + keys_position.push_back(-rot_val); + + rot_val = i*width_avgpool_sq; + keys_position.push_back(-rot_val); + } + for(int i =1; i< width_avgpool_out; i++){ + int rot_val = i*width_avgpool_out; + keys_position.push_back(i); + keys_position.push_back(-rot_val); + } + } + else if(stridingType == "single_channel"){ + for (int s=1; s FHEONANNController::generate_linear_rotation_positions(int maxFCLayeroutputs, int rotationPositions){ + + vector keys_position; + + for(int counter=0; counter>& kernelData, Ptext& biasInput, + int inputWidth, int inputChannels, int outputChannels, int kernelWidth, int paddingLen, int stride) { + + int kernelSq = kernelWidth * kernelWidth; + int inputSize = inputWidth * inputWidth; + int outputWidth = ((inputWidth - kernelWidth) / stride) + 1; + int outputSize = outputWidth * outputWidth; + int encode_level = encryptedInput->GetLevel(); + + // STEP 1 - Generate mixed mask for cleaning multi-channel inputs + int zero_elements = inputSize * (inputChannels - 1); + if(inputChannels < 2){ + zero_elements = inputSize; + } + vector mixed_mask = generate_mixed_mask(inputSize, zero_elements); + Ptext cleaning_mask = context->MakeCKKSPackedPlaintext(mixed_mask, 1, encode_level); + + vector mixed_mask_out = generate_mixed_mask(outputWidth, zero_elements); + Ptext cleaning_mask_out = context->MakeCKKSPackedPlaintext(mixed_mask_out, 1, encode_level); + + // STEP 2 - ROTATE INPUT TO FORM k^2 slices + vector rotated_ciphertexts; + for (int i = 0; i < kernelWidth; i++) { + if(i >0){ + encryptedInput = context->EvalRotate(encryptedInput, inputWidth); + } + rotated_ciphertexts.push_back(encryptedInput); + for (int j = 1; j < kernelWidth; j++) { + rotated_ciphertexts.push_back(context->EvalRotate(encryptedInput, j)); + } + } + + // STEP 3-6 - Convolution over all output channels + Ctext strided_cipher; + vector final_vec; + for (int out_ch = 0; out_ch < outputChannels; out_ch++) { + vector mult_results; + + // Per-kernel value multiplies + for (int k = 0; k < kernelSq; k++) { + mult_results.push_back(context->EvalMult(rotated_ciphertexts[k], kernelData[out_ch][k])); + } + + Ctext conv_sum = context->EvalAddMany(mult_results); + + // STEP 4 - Sum all input channels (rotating and adding) + if (inputChannels > 1) { + vector channel_sums = { conv_sum }; + for (int ch = 1; ch < inputChannels; ch++) { + conv_sum = context->EvalRotate(conv_sum, inputSize); + channel_sums.push_back(conv_sum); + } + conv_sum = context->EvalAddMany(channel_sums); + } + conv_sum = context->EvalMult(conv_sum, cleaning_mask); + + // STEP 5 - Striding + if (stride > 1) { + strided_cipher = downsample(conv_sum, inputWidth, stride); + } + else { + vector strided_vec; + for (int l = 0; l < outputWidth; l++) { + Ctext cleaned_cipher; + if (l == 0) { + cleaned_cipher = context->EvalMult(conv_sum, cleaning_mask_out); + } else { + conv_sum = context->EvalRotate(conv_sum, inputWidth); + cleaned_cipher = context->EvalRotate(context->EvalMult(conv_sum, cleaning_mask_out), -(outputWidth * l)); + } + strided_vec.push_back(cleaned_cipher); + } + strided_cipher = context->EvalAddMany(strided_vec); + } + + // STEP 7 - Rotate for output layout reconstruction + if (out_ch == 0) { + final_vec.push_back(strided_cipher); + } else { + final_vec.push_back(context->EvalRotate(strided_cipher, -(out_ch * outputSize))); + } + } + rotated_ciphertexts.clear(); + // STEP 8 - Add biases and return result + return context->EvalAdd(context->EvalAddMany(final_vec), biasInput);; +} + +/** +* @brief Perform a secure convolution operation with explicit padding + * on encrypted data. + * + * This function extends the standard secure convolution by explicitly handling + * zero-padding within the encrypted domain. The input ciphertext is expanded by + * adding zeros around the borders according to the specified padding size, + * after which the convolution is carried out as in the traditional setting. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param kernelData Convolution kernels represented as a 2D vector + * of plaintexts. + * @param biasInput Bias term for each output channel (plaintext). + * @param inputWidth Width of the input feature map (assumed square). + * @param kernelWidth Size of the convolution kernel (assumed square). + * @param padding Amount of zero-padding to apply. + * @param stride stride length for the convolution. + * @param inputChannels Number of input channels. + * @param outputChannels Number of output channels. + * + * @return Ctext Ciphertext representing the encrypted result + * of the convolution with padding. + * @see he_convolution() + */ +Ctext FHEONANNController::he_convolution_advanced(Ctext& encryptedInput, vector>& kernelData, Ptext& biasInput, + int inputWidth, int inputChannels, int outputChannels, int kernelWidth, int padding, int stride){ + + /** If padding is 0 */ + if(padding ==0){ + auto conv_basic_cipher = he_convolution(encryptedInput, kernelData, biasInput, inputWidth, + kernelWidth, inputChannels, outputChannels, padding, stride); + return conv_basic_cipher; + } + int padded_width = inputWidth + (2*padding); + int padded_width_sq = pow(padded_width, 2); + int width_sq = pow(inputWidth, 2); + int zeros_elements = ((inputChannels*width_sq) - inputWidth); + int encode_level = encryptedInput->GetLevel(); + auto padding_mix_mask = generate_mixed_mask(inputWidth, zeros_elements); + Ptext in_clean_mask = context->MakeCKKSPackedPlaintext(padding_mix_mask, 1, encode_level); + + /** generate vector of padding width */ + Ctext channel_cipher = encryptedInput; + vector channel_vector_ciphers; + for(int i = 0; i< inputChannels; i++){ + if(i!=0){ + channel_cipher = context->EvalRotate(channel_cipher, width_sq); + } + vector in_chan_vec; + Ctext in_chan_cipher = channel_cipher; + for(int k=0; kEvalMult(in_chan_cipher, in_clean_mask); + in_chan_cipher = context->EvalRotate(in_chan_cipher, inputWidth); + if(k==0){ + in_chan_vec.push_back(in_clean_cipher); + } + else{ + int in_rot_position = k*padded_width; + Ctext padded_cipher = context->EvalRotate(in_clean_cipher, -in_rot_position); + in_chan_vec.push_back(padded_cipher); + } + } + Ctext in_sum_cipher = context->EvalAddMany(in_chan_vec); + if(i==0){ + channel_vector_ciphers.push_back(in_sum_cipher); + } + else{ + int in_rotate = i*padded_width_sq; + Ctext in_rotate_cipher = context->EvalRotate(in_sum_cipher, -in_rotate); + channel_vector_ciphers.push_back(in_rotate_cipher); + } + } + int padd_extra = (padding*padded_width)+padding; + Ctext padded_cipher = context->EvalAddMany(channel_vector_ciphers); + if(padd_extra != 0){ + padded_cipher = context->EvalRotate(padded_cipher, -padd_extra); + } + Ctext conv_basic_cipher = he_convolution(padded_cipher, kernelData, biasInput, inputWidth, kernelWidth, + inputChannels, outputChannels, padding, stride); + return conv_basic_cipher; +} + +/** + * @brief Perform an optimized secure convolution for the special case + * of stride = 1, kernel size = 3, and padding = 1. + * + * This function implements a convolutional layer in the encrypted domain + * using homomorphic encryption, optimized specifically for the case where + * stride = 1, kernel size = 3, and padding = 3. By exploiting this fixed + * configuration, the function reduces the number of ciphertext rotations, + * multiplications, and additions compared to the generic secure convolution + * implementation, resulting in improved efficiency. + * + * It applies single channel striding given the striding value is greater than 1. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param kernelData Convolution kernels represented as a 2D vector + * of plaintexts. + * @param biasInput Bias term for each output channel (plaintext). + * @param inputWidth Width of the input feature map (assumed square). + * @param inputChannels Number of input channels. + * @param outputChannels Number of output channels. + * @param stride stride length (must be 1 for this optimized version). + * @param index Index of the current kernel or output channel + * being processed. + * + * @return Ctext Ciphertext representing the encrypted result + * of the optimized convolution. + * + * @note This optimized implementation should only be used when + * stride = 1, kernel size = 3, and padding = 3. For other cases, + * use @ref he_convolution or @ref he_convolution_advanced(). + * + * @see he_convolution() + * @see he_convolution_advanced() + * + * @warning Supplying parameters outside the supported configuration + * (stride ≠ 1, kernel size ≠ 3, padding ≠ 3) will lead to + * incorrect results. + */ +Ctext FHEONANNController::he_convolution_optimized(Ctext& encryptedInput, vector>& kernelData, Ptext& biasInput, + int inputWidth, int inputChannels, int outputChannels, int stride, int index){ + + int kernelSq = 9; + int inputSize = inputWidth*inputWidth; + int widthOut = inputWidth/stride; + int outputSize = widthOut*widthOut; + int encode_level = encryptedInput->GetLevel(); + vector rotated_ciphertexts; + + auto digits = context->EvalFastRotationPrecompute(encryptedInput); + Ctext first_shot = context->EvalFastRotation(encryptedInput, -1, context->GetCyclotomicOrder(), digits); + Ctext second_shot = context->EvalFastRotation(encryptedInput, 1, context->GetCyclotomicOrder(), digits); + rotated_ciphertexts.push_back(context->EvalRotate(first_shot, -inputWidth)); + rotated_ciphertexts.push_back(context->EvalFastRotation(encryptedInput, -inputWidth, context->GetCyclotomicOrder(), digits)); + rotated_ciphertexts.push_back(context->EvalRotate(second_shot, -inputWidth)); + rotated_ciphertexts.push_back(first_shot); + rotated_ciphertexts.push_back(encryptedInput); + rotated_ciphertexts.push_back(second_shot); + rotated_ciphertexts.push_back(context->EvalRotate(first_shot, inputWidth)); + rotated_ciphertexts.push_back(context->EvalFastRotation(encryptedInput, inputWidth, context->GetCyclotomicOrder(), digits)); + rotated_ciphertexts.push_back(context->EvalRotate(second_shot, inputWidth)); + Ptext cleaning_mask = context->MakeCKKSPackedPlaintext(generate_mixed_mask(inputSize, (inputChannels*inputSize)), 1, encode_level); + + vector kernelSum(kernelSq); + vector sumVec(inputChannels); + vector finalVec(outputChannels); + for(int outCh=0; outChEvalMult(rotated_ciphertexts[j], kernelData[outCh][j]); + } + sumVec[0] = context->EvalAddMany(kernelSum); + + /*** STEP 4: SUM RESULTS OF ALL INPUT CHANNELS ***/ + for(int k=1; kEvalRotate(sumVec[k-1], inputSize); + } + Ctext interCipher = context->EvalMult(context->EvalAddMany(sumVec), cleaning_mask); + /**** STEP 5: THIS IS EXTRACTING DATA FROM CONVOLUTION WITH STRIDING > 1 */ + if(stride != 1){ + interCipher = downsample(interCipher, inputWidth, stride); + } + /** STEP 7: ROTATE THE CIPHERTEXT FOR EACH CHANNEL TO RECONSTRUCT THE OUTPUT */ + if(outCh == 0){ + finalVec[outCh] = interCipher; + } + else{ + finalVec[outCh] = context->EvalRotate(interCipher, -outCh*outputSize); + } + } + + Ctext finalResults = context->EvalAdd(context->EvalAddMany(finalVec), biasInput); + finalVec.clear(); + sumVec.clear(); + kernelSum.clear(); + rotated_ciphertexts.clear(); + return finalResults; +} + + +/** + * @brief Perform secure convolution layer evaluation for the special case + * of stride = 1, kernel size = 3, and padding = 1. + * + * If striding is greater than 1, it applies striding across multiple channels simultaneously + * using multi_channels approach rather than channel-by-channel. + * This approach improves efficiency for FHE-based deep networks with multiple input channels. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param kernelData Convolution kernels for the main branch, represented + * as a 2D vector of plaintexts. + * @param biasInput Bias term for the main convolution branch (plaintext). + * @param inputWidth Width of the input feature map (assumed square). + * @param inputChannels Number of input channels. + * @param outputChannels Number of output channels (shared across both branches). + * + * @return Ctext A ciphertexts containing the encrypted results of the convolution + * + * @warning This function assumes that striding and channel-based optimization + * are supported by the encryption scheme. Using unsupported parameters + * may lead to incorrect results. + */ +Ctext FHEONANNController::he_convolution_optimized_with_multiple_channels(Ctext& encryptedInput, vector>& kernelData, + Ptext& biasInput, int inputWidth, int inputChannels, int outputChannels){ + + constexpr int stride = 2; + constexpr int kernelSq = 9; // Assuming 3x3 kernel (kernelWidthSq = 9) + int outputWidth = inputWidth / stride; + int inputSize = inputWidth * inputWidth; + int outputSize = outputWidth * outputWidth; + int encodeLevel = encryptedInput->GetLevel(); + + // Precompute rotations only once with minimal rotation set + vector rotatedInputs; + int vectorSize = inputSize * inputChannels; + Ptext cleaningMask = context->MakeCKKSPackedPlaintext(generate_mixed_mask(inputSize, vectorSize), 1, encodeLevel); + + Ptext cleaningoutputMask = context->MakeCKKSPackedPlaintext(generate_mixed_mask((inputChannels*outputSize), vectorSize), 1, encodeLevel); + + // Horizontal rotations + auto digits = context->EvalFastRotationPrecompute(encryptedInput); + Ctext first_shot = context->EvalFastRotation(encryptedInput, -1, context->GetCyclotomicOrder(), digits); + Ctext second_shot = context->EvalFastRotation(encryptedInput, 1, context->GetCyclotomicOrder(), digits); + rotatedInputs.push_back(context->EvalRotate(first_shot, -inputWidth)); + rotatedInputs.push_back(context->EvalFastRotation(encryptedInput, -inputWidth, context->GetCyclotomicOrder(), digits)); + rotatedInputs.push_back(context->EvalRotate(second_shot, -inputWidth)); + rotatedInputs.push_back(first_shot); + rotatedInputs.push_back(encryptedInput); + rotatedInputs.push_back(second_shot); + rotatedInputs.push_back(context->EvalRotate(first_shot, inputWidth)); + rotatedInputs.push_back(context->EvalFastRotation(encryptedInput, inputWidth, context->GetCyclotomicOrder(), digits)); + rotatedInputs.push_back(context->EvalRotate(second_shot, inputWidth)); + + // Create vectors to store results + int innerCount = 0; + int outCount = 0; + int outchanSize = outputChannels/inputChannels; + Ctext mainResult, shortcutResult; + vector mainResults(outchanSize); + vector inChannelsResults(inputChannels); + vector convChannelSum(inputChannels); + vector kernelSum(kernelSq); + // Process output channels with batch approach + for (int outCh = 0; outCh < outputChannels; outCh++) { + + // Apply convolution with batch channel processing + for (int j = 0; jEvalMult(rotatedInputs[j], kernelData[outCh][j]); + } + convChannelSum[0] = context->EvalAddMany(kernelSum); + for (int inCh = 1; inChEvalRotate(convChannelSum[inCh-1] , inputSize); + } + + if(innerCount == 0){ + inChannelsResults[innerCount] = context->EvalMult(context->EvalAddMany(convChannelSum), cleaningMask); + } + else{ + inChannelsResults[innerCount] = context->EvalRotate(context->EvalMult(context->EvalAddMany(convChannelSum), cleaningMask), (-innerCount*inputSize)); + } + + if(innerCount == inputChannels-1){ + mainResult = context->EvalAddMany(inChannelsResults); + mainResult = downsample_with_multiple_channels(mainResult, inputWidth, stride, inputChannels); + mainResult = context->EvalMult(mainResult, cleaningoutputMask); + shortcutResult = context->EvalMult(shortcutResult, cleaningoutputMask); + + if(outCount == 0){ + mainResults[outCount] = mainResult; + } + else{ + int rotateAmount = - outCount * (inputChannels * outputSize); + mainResults[outCount] = context->EvalRotate(mainResult, rotateAmount); + } + outCount++; + innerCount = 0; + } + else{ + innerCount++; + } + } + + // Combine results and add biases + Ctext finalMainResult = context->EvalAdd(context->EvalAddMany(mainResults), biasInput); + rotatedInputs.clear(); + mainResults.clear(); + convChannelSum.clear(); + return finalMainResult; +} + +/** + * @brief Perform a secure shortcut convolution (as used in ResNets) + * on encrypted data. + * + * This function implements the shortcut (or projection) convolution used + * in Residual Networks (ResNets). The shortcut convolution adjusts the + * dimensions of the input feature map so that it can be added to the + * output of a residual block. In the encrypted setting, this is carried + * out homomorphically on ciphertexts using pre-encrypted weights. + * + * Conceptually, the operation is identically equal to the convolution rather than it uses + * a kernel size = 1, striding = 2, padding = 1. + * It is ddefined in ResNet architectures, but performed securely in the FHE domain. + * We used the single channel striding approach in this implementation. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param kernelData Convolution kernel weights represented as a vector + * of plaintexts (projection filter). + * @param biasInput Bias term for the shortcut convolution (plaintext). + * @param inputWidth Width of the input feature map (assumed square). + * @param inputChannels Number of input channels. + * @param outputChannels Number of output channels (dimension after projection). + * + * @return Ctext Ciphertext representing the encrypted result + * of the shortcut convolution. + * + * @note This function is a special case of the standard convolution operation, + * optimized for use in residual connections of ResNets. + * + * @see he_convolution() + * @see he_convolution_advanced() + * @see he_convolution_optimized() + */ +Ctext FHEONANNController::he_shortcut_convolution(Ctext& encryptedInput, vector& kernelData, Ptext& biasInput, + int inputWidth, int inputChannels, int outputChannels){ + + int width_sq = pow(inputWidth, 2); + int stride = 2; + int width_out = (inputWidth/stride); + int width_out_sq = pow(width_out, 2); + int encode_level = encryptedInput->GetLevel(); + int noSlots = inputChannels*width_sq; + + int zeros_elements = width_sq*(inputChannels-1); + auto mixed_mask = generate_mixed_mask(width_sq, zeros_elements); + Ptext cleaning_mask = context->MakeCKKSPackedPlaintext(mixed_mask, 1, encode_level, nullptr, noSlots); + + /*** we do this in a loop contains the output layers since this is repeated for every output layer */ + vector final_vec(outputChannels); + vector sum_vec(inputChannels); + Ctext interCipher; + for(int i=0; iEvalMult(encryptedInput, kernelData[i]); + + /*** STEP 4: SUM RESULTS OF ALL INPUT CHANNELS **/ + for(int k=1; kEvalRotate(sum_vec[k-1], width_sq); + } + interCipher = context->EvalMult(context->EvalAddMany(sum_vec), cleaning_mask); + + /**** STEP 5: THIS IS EXTRACTING DATA FROM CONVOLUTION WITH STRIDING > 1 */ + interCipher = downsample(interCipher, inputWidth, stride); + + /** STEP 7: ROTATE THE CIPHERTEXT FOR EACH CHANNEL TO RECONSTRUCT THE OUTPUT */ + if(i == 0){ + final_vec[0] = interCipher; + } + else{ + final_vec[i] = context->EvalRotate(interCipher, -(i*width_out_sq)); + } + } + + Ctext finalResults = context->EvalAdd(context->EvalAddMany(final_vec), biasInput); + final_vec.clear(); + sum_vec.clear(); + return finalResults; +} + +/** + * @brief Perform a combined secure convolution with stride-2 and shortcut + * projection for ResNet blocks. + * + * This function implements a custom operation tailored for ResNet + * architectures in the encrypted domain. It simultaneously evaluates: + * 1. The main convolution branch with a stride of 2 (downsampling). + * 2. The shortcut (projection) branch to match dimensions. + * + * By handling both the strided convolution and the shortcut convolution in + * one function, it enables efficient construction of ResNet blocks without + * relying on the generic optimized convolution with integrated striding. + * It uses single channel striding approach. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param kernelData Convolution kernels for the main branch, + * represented as a 2D vector of plaintexts. + * @param shortcutKernelData Convolution kernel weights for the shortcut + * projection branch (plaintexts). + * @param biasInput Bias term for the main convolution branch (plaintext). + * @param shortcutBiasVector Bias term for the shortcut branch (plaintext). + * @param inputWidth Width of the input feature map (assumed square). + * @param inputChannels Number of input channels. + * @param outputChannels Number of output channels (shared across both branches). + * + * @return vector A vector of ciphertexts containing both the + * encrypted results of the main branch and the + * shortcut branch, which can then be combined + * (via ciphertext addition) to form the ResNet + * residual output. + * + * @note This function is specific to ResNet-style blocks with stride-2 + * downsampling. For other convolution cases, use + * @ref he_convolution() or @ref he_convolution_optimized(). + * + * @see he_shortcut_convolution() + * @see he_convolution_optimized() + * + * @warning This function assumes stride = 2 in the main convolution branch. + * Supplying different stride values may lead to incorrect results. + */ +vector FHEONANNController::he_convolution_and_shortcut_optimized( const Ctext& encryptedInput, const vector>& kernelData, + const vector& shortcutKernelData, Ptext& biasInput, Ptext& shortcutBiasVector, + int inputWidth, int inputChannels, int outputChannels){ + constexpr int stride = 2; + constexpr int kernelSq = 9; // Assuming 3x3 kernel (kernelWidthSq = 9) + int outputWidth = inputWidth / stride; + int inputSize = inputWidth * inputWidth; + int outputSize = outputWidth * outputWidth; + int encodeLevel = encryptedInput->GetLevel(); + + // Precompute rotations only once with minimal rotation set + vector rotatedInputs; + int vectorSize = inputSize * inputChannels; + Ptext cleaningMask = context->MakeCKKSPackedPlaintext(generate_mixed_mask(inputSize, vectorSize), 1, encodeLevel); + + // Horizontal rotations + auto digits = context->EvalFastRotationPrecompute(encryptedInput); + Ctext first_shot = context->EvalFastRotation(encryptedInput, -1, context->GetCyclotomicOrder(), digits); + Ctext second_shot = context->EvalFastRotation(encryptedInput, 1, context->GetCyclotomicOrder(), digits); + rotatedInputs.push_back(context->EvalRotate(first_shot, -inputWidth)); + rotatedInputs.push_back(context->EvalFastRotation(encryptedInput, -inputWidth, context->GetCyclotomicOrder(), digits)); + rotatedInputs.push_back(context->EvalRotate(second_shot, -inputWidth)); + rotatedInputs.push_back(first_shot); + rotatedInputs.push_back(encryptedInput); + rotatedInputs.push_back(second_shot); + rotatedInputs.push_back(context->EvalRotate(first_shot, inputWidth)); + rotatedInputs.push_back(context->EvalFastRotation(encryptedInput, inputWidth, context->GetCyclotomicOrder(), digits)); + rotatedInputs.push_back(context->EvalRotate(second_shot, inputWidth)); + + // Create vectors to store results + vector convChannelSum(inputChannels), shortcutChannelSum(inputChannels); + vector mainResults(outputChannels), shortcutResults(outputChannels); + vector kernelSum(kernelSq); + Ctext mainResult, shortcutResult; + + // Process output channels with batch approach + for (int outCh = 0; outCh < outputChannels; outCh++) { + + // Apply convolution with batch channel processing + for (int j = 0; j < kernelSq; ++j) { + kernelSum[j] = context->EvalMult(rotatedInputs[j], kernelData[outCh][j]); + } + convChannelSum[0] = context->EvalAddMany(kernelSum); + shortcutChannelSum[0] = context->EvalMult(encryptedInput, shortcutKernelData[outCh]); + + for (int inCh = 1; inCh < inputChannels; ++inCh) { + convChannelSum[inCh] = context->EvalRotate(convChannelSum[inCh-1] , inputSize); + shortcutChannelSum[inCh] = context->EvalRotate(shortcutChannelSum[inCh-1], inputSize); + } + + mainResult = context->EvalMult(context->EvalAddMany(convChannelSum), cleaningMask); + shortcutResult = context->EvalMult(context->EvalAddMany(shortcutChannelSum), cleaningMask); + + /** Compute Striding */ + mainResult = downsample(mainResult, inputWidth, stride); + shortcutResult = downsample(shortcutResult, inputWidth, stride); + + if( outCh==0 ){ + mainResults[outCh] = mainResult; + shortcutResults[outCh] = shortcutResult; + } + else{ + int rotateAmount = - outCh * outputSize; + // cout <<"Rotattion Positon: " << rotateAmount << endl; + mainResults[outCh] = context->EvalRotate(mainResult, rotateAmount); + shortcutResults[outCh] = context->EvalRotate(shortcutResult, rotateAmount); + } + } + + // Combine results and add biases + Ctext finalMainResult = context->EvalAdd(context->EvalAddMany(mainResults), biasInput); + Ctext finalShortcutResult = context->EvalAdd(context->EvalAddMany(shortcutResults), shortcutBiasVector); + + rotatedInputs.clear(); + mainResults.clear(); + shortcutResults.clear(); + convChannelSum.clear(); + shortcutChannelSum.clear(); + return {finalMainResult, finalShortcutResult}; +} + +/** + * @brief Perform a channel-optimized secure convolution and shortcut evaluation + * for ResNet blocks. + * + * This function is a custom ResNet-specific operation in the encrypted domain. + * It evaluates the shortcut convolution across multiple channels simultaneously + * rather than channel-by-channel, while also computing the main convolution + * branch with integrated striding. This approach improves efficiency for FHE-based + * ResNet implementations by reducing the number of rotations and multiplications. + * It uses multi channel striding approach. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param kernelData Convolution kernels for the main branch, represented + * as a 2D vector of plaintexts. + * @param shortcutKernelData Convolution kernels for the shortcut branch + * (plaintexts). + * @param biasInput Bias term for the main convolution branch (plaintext). + * @param shortcutBiasInput Bias term for the shortcut branch (plaintext). + * @param inputWidth Width of the input feature map (assumed square). + * @param inputChannels Number of input channels. + * @param outputChannels Number of output channels (shared across both branches). + * + * @return vector A vector of ciphertexts containing the encrypted + * results of both the main branch and shortcut + * branch, ready to be combined for the ResNet residual output. + * + * @note This function is optimized for performing shortcut convolutions across + * multiple channels simultaneously. It is recommended for FHE-based + * ResNet implementations to reduce computation overhead. + * + * @see he_convolution_optimized() + * @see he_shortcut_convolution() + * + * @warning This function assumes that striding and channel-based optimization + * are supported by the encryption scheme. Using unsupported parameters + * may lead to incorrect results. + */ +vector FHEONANNController::he_convolution_and_shortcut_optimized_with_multiple_channels( const Ctext& encryptedInput, const vector>& kernelData, + const vector& shortcutKernelData, Ptext& biasInput, Ptext& shortcutBiasInput, + int inputWidth, int inputChannels, int outputChannels){ + constexpr int stride = 2; + constexpr int kernelSq = 9; // Assuming 3x3 kernel (kernelWidthSq = 9) + int outputWidth = inputWidth / stride; + int inputSize = inputWidth * inputWidth; + int outputSize = outputWidth * outputWidth; + int encodeLevel = encryptedInput->GetLevel(); + + // Precompute rotations only once with minimal rotation set + vector rotatedInputs; + int vectorSize = inputSize * inputChannels; + Ptext cleaningMask = context->MakeCKKSPackedPlaintext(generate_mixed_mask(inputSize, vectorSize), 1, encodeLevel); + + Ptext cleaningoutputMask = context->MakeCKKSPackedPlaintext(generate_mixed_mask((inputChannels*outputSize), vectorSize), 1, encodeLevel); + + // Horizontal rotations + auto digits = context->EvalFastRotationPrecompute(encryptedInput); + Ctext first_shot = context->EvalFastRotation(encryptedInput, -1, context->GetCyclotomicOrder(), digits); + Ctext second_shot = context->EvalFastRotation(encryptedInput, 1, context->GetCyclotomicOrder(), digits); + rotatedInputs.push_back(context->EvalRotate(first_shot, -inputWidth)); + rotatedInputs.push_back(context->EvalFastRotation(encryptedInput, -inputWidth, context->GetCyclotomicOrder(), digits)); + rotatedInputs.push_back(context->EvalRotate(second_shot, -inputWidth)); + rotatedInputs.push_back(first_shot); + rotatedInputs.push_back(encryptedInput); + rotatedInputs.push_back(second_shot); + rotatedInputs.push_back(context->EvalRotate(first_shot, inputWidth)); + rotatedInputs.push_back(context->EvalFastRotation(encryptedInput, inputWidth, context->GetCyclotomicOrder(), digits)); + rotatedInputs.push_back(context->EvalRotate(second_shot, inputWidth)); + + // Create vectors to store results + int innerCount = 0; + int outCount = 0; + int outchanSize = outputChannels/inputChannels; + Ctext mainResult, shortcutResult; + vector mainResults(outchanSize), shortcutResults(outchanSize); + vector inChannelsResults(inputChannels), inshortcutResults(inputChannels); + vector convChannelSum(inputChannels), shortcutChannelSum(inputChannels); + vector kernelSum(kernelSq); + + // Process output channels with batch approach + for (int outCh = 0; outCh < outputChannels; outCh++) { + + // Apply convolution with batch channel processing + for (int j = 0; jEvalMult(rotatedInputs[j], kernelData[outCh][j]); + } + convChannelSum[0] = context->EvalAddMany(kernelSum); + shortcutChannelSum[0] = context->EvalMult(encryptedInput, shortcutKernelData[outCh]); + + for (int inCh = 1; inChEvalRotate(convChannelSum[inCh-1] , inputSize); + shortcutChannelSum[inCh] = context->EvalRotate(shortcutChannelSum[inCh-1], inputSize); + } + + if(innerCount == 0){ + inChannelsResults[innerCount] = context->EvalMult(context->EvalAddMany(convChannelSum), cleaningMask); + inshortcutResults[innerCount] = context->EvalMult(context->EvalAddMany(shortcutChannelSum), cleaningMask); + } + else{ + inChannelsResults[innerCount] = context->EvalRotate(context->EvalMult(context->EvalAddMany(convChannelSum), cleaningMask), (-innerCount*inputSize)); + inshortcutResults[innerCount] = context->EvalRotate(context->EvalMult(context->EvalAddMany(shortcutChannelSum), cleaningMask), (-innerCount*inputSize)); + } + + if(innerCount == inputChannels-1){ + mainResult = context->EvalAddMany(inChannelsResults); + shortcutResult = context->EvalAddMany(inshortcutResults); + mainResult = downsample_with_multiple_channels(mainResult, inputWidth, stride, inputChannels); + shortcutResult = downsample_with_multiple_channels(shortcutResult, inputWidth,stride, inputChannels); + mainResult = context->EvalMult(mainResult, cleaningoutputMask); + shortcutResult = context->EvalMult(shortcutResult, cleaningoutputMask); + + if(outCount == 0){ + mainResults[outCount] = mainResult; + shortcutResults[outCount] = shortcutResult; + } + else{ + + int rotateAmount = - outCount * (inputChannels * outputSize); + mainResults[outCount] = context->EvalRotate(mainResult, rotateAmount); + shortcutResults[outCount] = context->EvalRotate(shortcutResult, rotateAmount); + } + outCount++; + innerCount = 0; + } + else{ + innerCount++; + } + } + + // Combine results and add biases + Ctext finalMainResult = context->EvalAdd(context->EvalAddMany(mainResults), biasInput); + Ctext finalShortcutResult = context->EvalAdd(context->EvalAddMany(shortcutResults), shortcutBiasInput); + + rotatedInputs.clear(); + mainResults.clear(); + shortcutResults.clear(); + convChannelSum.clear(); + shortcutChannelSum.clear(); + return {finalMainResult, finalShortcutResult}; +} + +/** + * @brief Perform a secure average pooling operation on encrypted data. + * + * This function implements average pooling in the encrypted domain using + * homomorphic encryption. Given an encrypted input feature map, it applies + * pooling with the specified kernel size and stride, aggregating values + * across local regions while keeping the data encrypted. + * it uses the single channel by channel striding approach. + * It can also handle poolings of all kernel sizes and striding values. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param inputWidth Width of the input feature map (assumed square). + * @param inputChannels Number of input channels. + * @param kernelWidth Width of the pooling kernel (assumed square). + * @param stride stride length used for the pooling operation. + * + * @return Ctext Ciphertext representing the encrypted result + * of the average pooling operation. + * + * @see generate_avgpool_rotation_positions() + * @see generate_avgpool_optimized_rotation_positions() + */ +Ctext FHEONANNController::he_avgpool(Ctext encryptedInput, int inputWidth, int inputChannels, int kernelWidth, int stride){ + + int outputWidth = inputWidth/stride; + int kernelSq = pow(kernelWidth, 2); + int inputSize = pow(inputWidth, 2); + int outputSize = pow(outputWidth, 2); + int encode_level = encryptedInput->GetLevel(); + + /*** STEP 1 - ROTATE THE CIPHERTEXT into by k^2-1 and create a k^2 rotated right positions ***/ + vector rotated_ciphertexts; + for (int i = 0; i < kernelWidth; i++) { + if(i >0){ + encryptedInput = context->EvalRotate(encryptedInput, inputWidth); + } + rotated_ciphertexts.push_back(encryptedInput); + for (int j = 1; j < kernelWidth; j++) { + rotated_ciphertexts.push_back(context->EvalRotate(encryptedInput, j)); + } + } + + /**** STEP 2: Sum the rotated ciphertext */ + Ctext sum_cipher = context->EvalAddMany(rotated_ciphertexts); + vector channel_ciphers; + if(inputWidth <= 2){ + for(int i = 1; iEvalRotate(sum_cipher, inputSize); + channel_ciphers.push_back(sum_cipher); + } + return context->EvalMerge(channel_ciphers); + } + + /*** STEP 3: Multiply the scale value with the sum cipher */ + int num_of_elements = inputChannels*inputSize; + auto masked_data = generate_scale_mask(kernelSq, num_of_elements); + auto masked_cipher = context->MakeCKKSPackedPlaintext(masked_data, 1, encode_level); + sum_cipher = context->EvalMult(sum_cipher, masked_cipher); + + /*** STEP 4: Extract the values needed in the ciphertext */ + Ctext strided_cipher = downsample(sum_cipher, inputWidth, stride); + channel_ciphers.push_back(strided_cipher); + for(int i = 1; iEvalRotate(sum_cipher, inputSize); + channel_ciphers.push_back(context->EvalRotate(downsample(sum_cipher, inputWidth, stride), -(i*outputSize))); + } + Ctext finalResults = context->EvalAddMany(channel_ciphers); + channel_ciphers.clear(); + rotated_ciphertexts.clear(); + return finalResults; +} + +/** + * @brief Perform a secure average pooling operation with padding and custom stride + * on encrypted data. + * + * This function implements average pooling in the encrypted domain using + * homomorphic encryption, allowing explicit control over stride and padding. + * When `padding` is greater than zero, zeros are added around the input + * feature map before pooling. This ensures correct output dimensions and + * simulates standard pooling behavior in plaintext settings. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param inputWidth Width of the input feature map (assumed square). + * @param outputChannels Number of output channels. + * @param kernelWidth Width of the pooling kernel (assumed square). + * @param stride stride length for the pooling operation. + * @param padding Amount of zero-padding applied around the input. + * + * @return Ctext Ciphertext representing the encrypted result + * of the advanced average pooling operation. + * + * @note This function is designed for FHE-based ANN implementations where + * padding cannot be applied directly to plaintexts. Rotations and + * additions on ciphertexts simulate the padding and pooling behavior. + * + * @see he_avgpool() + * @see generate_avgpool_rotation_positions() + * @see generate_avgpool_optimized_rotation_positions() + */ +Ctext FHEONANNController::he_avgpool_advanced(Ctext encryptedInput, int inputWidth, int outputChannels, + int kernelWidth, int stride, int padding){ + + int encode_level = encryptedInput->GetLevel(); + if(padding ==0){ + auto avgpool_cipher = he_avgpool(encryptedInput, inputWidth, outputChannels, kernelWidth, stride); + return avgpool_cipher; + } + int padded_width = inputWidth + (2*padding); + int padded_width_sq = pow(padded_width, 2); + int width_sq = pow(inputWidth, 2); + int zeros_elements = ((outputChannels*width_sq) - inputWidth); + auto padding_mix_mask = generate_mixed_mask(inputWidth, zeros_elements); + Ptext in_clean_mask = context->MakeCKKSPackedPlaintext(padding_mix_mask, 1, encode_level); + + /** generate vector of padding width */ + Ctext channel_cipher = encryptedInput; + vector channel_vector_ciphers; + for(int i = 0; i< outputChannels; i++){ + if(i!=0){ + channel_cipher = context->EvalRotate(channel_cipher, width_sq); + } + vector in_chan_vec; + Ctext in_chan_cipher = channel_cipher; + for(int k=0; kEvalMult(in_chan_cipher, in_clean_mask); + in_chan_cipher = context->EvalRotate(in_chan_cipher, inputWidth); + if(k==0){ + in_chan_vec.push_back(in_clean_cipher); + } + else{ + int in_rot_position = k*padded_width; + Ctext padded_cipher = context->EvalRotate(in_clean_cipher, -in_rot_position); + in_chan_vec.push_back(padded_cipher); + } + } + Ctext in_sum_cipher = context->EvalAddMany(in_chan_vec); + if(i==0){ + channel_vector_ciphers.push_back(in_sum_cipher); + } + else{ + int in_rotate = i*padded_width_sq; + Ctext in_rotate_cipher = context->EvalRotate(in_sum_cipher, -in_rotate); + channel_vector_ciphers.push_back(in_rotate_cipher); + } + } + int padd_extra = (padding*padded_width)+padding; + Ctext padded_cipher = context->EvalAddMany(channel_vector_ciphers); + if(padd_extra != 0){ + padded_cipher = context->EvalRotate(padded_cipher, -padd_extra); + } + Ctext avgpool_cipher = he_avgpool(padded_cipher, inputWidth, outputChannels, kernelWidth, stride); + return avgpool_cipher; +} + +/** + * @brief Perform an optimized secure average pooling operation on encrypted data. + * + * This function implements an optimized version of average pooling in the + * encrypted domain using homomorphic encryption. It computes the average + * over local regions efficiently, reducing the number of ciphertext rotations + * and additions compared to the standard `he_avgpool` implementation. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param inputWidth Width of the input feature map (assumed square). + * @param inputChannels Number of input channels. + * @param kernelWidth Width of the pooling kernel (assumed square). + * @param stride stride length for the pooling operation. + * + * @return Ctext Ciphertext representing the encrypted result + * of the optimized average pooling operation. + * + * @note This function is optimized for FHE-based ANN implementations where + * reducing the number of rotations and multiplications is critical for + * efficiency. + * + * @see he_avgpool() + * @see he_avgpool__advanced() + * @see generate_avgpool_optimized_rotation_positions() + */ +Ctext FHEONANNController::he_avgpool_optimzed(Ctext& encryptedInput, int inputWidth, int inputChannels, int kernelWidth, int stride){ + + int kernelSq = pow(kernelWidth, 2); + int inputSize = pow(inputWidth, 2); + int outputWidth = inputWidth/stride; + int outputSize = pow(outputWidth, 2); + int encode_level = encryptedInput->GetLevel(); + + /*** STEP 1 - ROTATE THE CIPHERTEXT into by k^2-1 and create a k^2 rotated right positions ***/ + vector rotated_ciphertexts; + auto digits = context->EvalFastRotationPrecompute(encryptedInput); + rotated_ciphertexts.push_back(encryptedInput); + rotated_ciphertexts.push_back(context->EvalFastRotation(encryptedInput, 1, context->GetCyclotomicOrder(), digits)); + rotated_ciphertexts.push_back(context->EvalFastRotation(encryptedInput, inputWidth, context->GetCyclotomicOrder(), digits)); + rotated_ciphertexts.push_back(context->EvalRotate(context->EvalFastRotation(encryptedInput, inputWidth, context->GetCyclotomicOrder(), digits), 1)); + Ctext sum_cipher = context->EvalAddMany(rotated_ciphertexts); + + /*** STEP 3: Multiply the scale value with the sum cipher */ + int num_of_elements = inputChannels*inputSize; + auto masked_data = generate_scale_mask(kernelSq, num_of_elements); + auto masked_cipher = context->MakeCKKSPackedPlaintext(masked_data, 1, encode_level); + sum_cipher = context->EvalMult(sum_cipher, masked_cipher); + + vector channel_ciphers; + + /**** Caryout the average pooling ofif we have just 3 elements in a channel */ + if(inputWidth <= 2){ + for(int i = 1; iEvalRotate(sum_cipher, inputSize); + channel_ciphers.push_back(sum_cipher); + } + return context->EvalMerge(channel_ciphers); + } + + /*** STEP 4: Extract the values needed in the ciphertext */ + Ctext strided_cipher = downsample(sum_cipher, inputWidth, stride); + channel_ciphers.push_back(strided_cipher); + for(int i = 1; iEvalRotate(sum_cipher, inputSize); + channel_ciphers.push_back(context->EvalRotate(downsample(sum_cipher, inputWidth, stride), -i*outputSize)); + } + Ctext finalResult = context->EvalAddMany(channel_ciphers); + channel_ciphers.clear(); + return finalResult; +} + + +/** + * @brief Perform an optimized secure average pooling operation on encrypted data. + * + * This function implements an optimized version of average pooling in the + * encrypted domain using homomorphic encryption. It computes the average + * over local regions efficiently, reducing the number of ciphertext rotations + * and additions compared to the standard `he_avgpool` implementation. + * It allows striding over all output channels at once. Most efficient pooling + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param inputWidth Width of the input feature map (assumed square). + * @param inputChannels Number of input channels. + * @param kernelWidth Width of the pooling kernel (assumed square). + * @param stride stride length for the pooling operation. + * + * @return Ctext Ciphertext representing the encrypted result + * of the optimized average pooling operation. + * + * @note This function is optimized for FHE-based ANN implementations where + * reducing the number of rotations and multiplications is critical for + * efficiency. + * + * @see he_avgpool() + * @see he_avgpool__advanced() + * @see generate_avgpool_optimized_rotation_positions() + */ +Ctext FHEONANNController::he_avgpool_optimzed_with_multiple_channels(Ctext& encryptedInput, int inputWidth, int inputChannels, int kernelWidth, int stride){ + + int kernelSq = pow(kernelWidth, 2); + int inputSize = pow(inputWidth, 2); + int encode_level = encryptedInput->GetLevel(); + + /*** STEP 1 - ROTATE THE CIPHERTEXT into by k^2-1 and create a k^2 rotated right positions ***/ + vector rotated_ciphertexts; + auto digits = context->EvalFastRotationPrecompute(encryptedInput); + rotated_ciphertexts.push_back(encryptedInput); + rotated_ciphertexts.push_back(context->EvalFastRotation(encryptedInput, 1, context->GetCyclotomicOrder(), digits)); + rotated_ciphertexts.push_back(context->EvalFastRotation(encryptedInput, inputWidth, context->GetCyclotomicOrder(), digits)); + rotated_ciphertexts.push_back(context->EvalRotate(context->EvalFastRotation(encryptedInput, inputWidth, context->GetCyclotomicOrder(), digits), 1)); + Ctext sum_cipher = context->EvalAddMany(rotated_ciphertexts); + + /*** STEP 3: Multiply the scale value with the sum cipher */ + int num_of_elements = inputChannels*inputSize; + auto masked_data = generate_scale_mask(kernelSq, num_of_elements); + auto masked_cipher = context->MakeCKKSPackedPlaintext(masked_data, 1, encode_level); + sum_cipher = context->EvalMult(sum_cipher, masked_cipher); + + vector channel_ciphers; + + /**** Caryout the average pooling ofif we have just 3 elements in a channel */ + if(inputWidth <= 2){ + for(int i = 1; iEvalRotate(sum_cipher, inputSize); + channel_ciphers.push_back(sum_cipher); + } + return context->EvalMerge(channel_ciphers); + } + + Ctext finalResult = downsample_with_multiple_channels(sum_cipher, inputWidth, stride, inputChannels); + return finalResult; +} + +/**** Needed for ResNet Blocks */ +Ctext FHEONANNController::he_sum_two_ciphertexts(Ctext& firstInput, Ctext& secondInput){ + Ctext sumCipher = context->EvalAdd(firstInput, secondInput); + return sumCipher; +} + +/** + * @brief Perform a secure global average pooling operation on encrypted data. + * + * This function reduces each channel of the input feature map to a single value + * by averaging all elements in the channel. It is particularly useful in ResNet + * architectures where global pooling is applied before the fully connected layer. + * + * @param encryptedInput Encrypted input feature map (ciphertext). + * @param inputWidth Width of the input feature map (assumed square). + * @param outputChannels Number of output channels. + * @param kernelWidth Kernel size used for pooling (typically equal to inputWidth + * for global pooling, but included for flexibility). + * @param rotatePositions Precomputed rotation positions used to perform the + * homomorphic averaging across all elements in the channel. + * + * @return Ctext Ciphertext representing the encrypted result + * of the global average pooling operation. + * + * @note This function is optimized for FHE-based ResNet implementations. + * Reducing each channel to a single value minimizes the number of + * ciphertexts before the fully connected layer, improving efficiency. + * + * @see he_avgpool() + * @see he_avgpool__advanced() + * @see he_avgpool_optimzed() + */ +Ctext FHEONANNController::he_globalavgpool(Ctext& encryptedInput, int inputWidth, + int outputChannels, int kernelWidth, int rotatePositions){ + + // int encode_level = encryptedInput->GetLevel(); + /**** STEP 2: Sum the rotated ciphertext */ + // Ctext sum_cipher = context->EvalAddMany(rotated_ciphertexts); + int width_sq = inputWidth*inputWidth; + // int zero_elements = outputChannels*pow(kernelWidth, 2); + auto masked_cipher = context->MakeCKKSPackedPlaintext(generate_scale_mask(width_sq, outputChannels), 1); + // auto mixed_masked_cipher = context->MakeCKKSPackedPlaintext(generate_mixed_mask(width_sq, zero_elements), 1); + + /*** STEP 4: Extract the values needed in the ciphertext + **** Generate ciphers = number of output channels rotating at width^2 + **** rotate at each cipher at w and extract values in w *********/ + vector channel_ciphers; + vector result_ciphers; + Ctext in_rot_ciphertext; + int rotation_index = 0; + int j = 0; + + for(int i = 0; i< outputChannels; i++){ + if(i != 0){ + encryptedInput = context->EvalRotate(encryptedInput, width_sq); + } + // Ctext sumRe = context->EvalMult(encryptedInput, mixed_masked_cipher); + result_ciphers.push_back(context->EvalSum(encryptedInput, width_sq)); + j+=1; + + /** check whether is equal to imgcols, merge them and rotate by imgCols. + * If i is equal to the outputSize, merge and rotate by imgCols */ + if(j == rotatePositions || i == (outputChannels-1)){ + if(rotation_index > 0){ + channel_ciphers.push_back(context->EvalRotate(context->EvalMerge(result_ciphers), -rotation_index)); + } + else{ + Ctext merged = context->EvalMerge(result_ciphers); + channel_ciphers.push_back(merged); + // cout << "merged" << endl; + } + rotation_index += rotatePositions; + result_ciphers.clear(); + j=0; + } + } + + Ctext fResults = context->EvalAddMany(channel_ciphers); + channel_ciphers.clear(); + result_ciphers.clear(); + return context->EvalMult(fResults, masked_cipher); +} + +/** + * @brief Perform a secure fully connected (linear) layer operation on encrypted data. + * + * This function implements a fully connected (dense) layer in the encrypted domain + * using homomorphic encryption. Given an encrypted input vector (e.g., the output + * of convolution or pooling layers), a plaintext weight matrix, and a bias vector, + * it computes the linear transformation: + * + * \f[ y = \sum_i w_i \cdot x_i + b \f] + * + * entirely on ciphertexts. + * + * @param encryptedInput Encrypted input vector (ciphertext) of size `inputSize`. + * @param weightMatrix Weight matrix for the fully connected layer, represented + * as a vector of plaintexts. + * @param biasInput Bias term for each output neuron (plaintext). + * @param inputSize Number of input features. + * @param outputSize Number of output neurons. + * @param rotatePositions Precomputed rotation positions required to perform + * the homomorphic summation across input features. + * + * @return Ctext Ciphertext representing the encrypted result + * of the fully connected layer. + * + * @see generate_linear_rotation_positions() + */ +Ctext FHEONANNController::he_linear(Ctext& encryptedInput, vector& weightMatrix, Ptext& biasInput, + int inputSize, int outputSize, int rotatePositions){ + + int output_size = weightMatrix.size(); + if(outputSize > output_size){ + /** need to handle error here because outputSize should never be grater than output_size */ + cout << "There is an error: ouputsize cannot be larger than weightedMatrix" << endl; + return encryptedInput; + } + /* calculate the results of weights * encrypted vector + bais. + Shit the results by number of elements in inputsize*iteration value */ + vector result_matrix; + vector inner_matrix; + int j = 0; + int rotation_index = 0; + for(int i = 0; i < outputSize; i++){ + inner_matrix.push_back(context->EvalSum(context->EvalMult(encryptedInput, weightMatrix[i]), inputSize)); + j+=1; + /** check whether is equal to imgcols, merge them and rotate by imgCols. + * If i is equal to the outputSize, merge and rotate by imgCols */ + if(j == rotatePositions || i == (outputSize-1)){ + if(rotation_index > 0){ + result_matrix.push_back(context->EvalRotate(context->EvalMerge(inner_matrix), -rotation_index)); + } + else{ + result_matrix.push_back(context->EvalMerge(inner_matrix)); + } + inner_matrix.clear(); + rotation_index +=rotatePositions; + j=0; + + } + } + + /**** convert everything to one vector. and add the biasInput ***/ + Ctext fResults = context->EvalAdd(context->EvalAddMany(result_matrix), biasInput); + inner_matrix.clear(); + result_matrix.clear(); + return fResults; +} + +/** + * @brief Perform an optimized secure fully connected (linear) layer operation + * on encrypted data. + * + * This function computes the fully connected layer in the encrypted domain using + * homomorphic encryption, optimizing the summation and multiplication of weights + * and inputs to reduce the number of rotations and homomorphic operations. + * It merges all computed values to produce the final output ciphertext. + * + * @param encryptedInput Encrypted input vector (ciphertext) of size `inputSize`. + * @param weightMatrix Weight matrix for the fully connected layer, represented + * as a vector of plaintexts. + * @param biasInput Bias term for each output neuron (plaintext). + * @param inputSize Number of input features. + * @param outputSize Number of output neurons. + * + * @return Ctext Ciphertext representing the encrypted result + * + * @see he_linear() + * @see generate_linear_rotation_positions() + */ +Ctext FHEONANNController::he_linear_optimized(Ctext& encryptedInput, vector& weightMatrix, + Ptext& biasInput, int inputSize, int outputSize){ + + int output_size = weightMatrix.size(); + if(outputSize > output_size){ + /** need to handle error here because outputSize should never be grater than output_size */ + cout << "There is an error: ouputsize cannot be larger than weightedMatrix" << endl; + return encryptedInput; + } + /* calculate the results of weights * encrypted vector + bais. + Shit the results by number of elements in inputsize*iteration value */ + vector inner_matrix; + for(int i = 0; i < outputSize; i++){ + inner_matrix.push_back(context->EvalSum(context->EvalMult(encryptedInput, weightMatrix[i]), inputSize)); + } + + return context->EvalAdd(context->EvalMerge(inner_matrix), biasInput); +} + +/** + * @brief Apply a secure ReLU activation on encrypted data using a Chebyshev polynomial approximation. + * + * This function approximates the ReLU function on ciphertexts using the EvalChebyFunction method. + * Input values are first scaled to the range [-1, 1] to improve the accuracy of the polynomial approximation. + * The `polyDegree` parameter determines the degree of the Chebyshev polynomial used for the approximation. + * + * @param encryptedInput Encrypted input vector (ciphertext). + * @param scaleValue Scaling factor to normalize input values to [-1, 1]. + * @param vectorSize Number of elements in the input vector. + * @param polyDegree Degree of the Chebyshev polynomial used for the ReLU approximation. + * + * @return Ctext Ciphertext representing the encrypted result of the ReLU activation. + * + * @see EvalChebyFunction() + */ +Ctext FHEONANNController::he_relu(Ctext& encryptedInput, double scaleValue, int vectorSize, int polyDegree) { + double lowerBound = -1; + double upperBound = 1; + + // scaleValue = 2*scaleValue; + + auto encryptInn = encryptedInput->Clone(); + if(scaleValue > 1){ + // scaleValue = 2*scaleValue; + auto mask_data = context->MakeCKKSPackedPlaintext(generate_scale_mask(scaleValue, vectorSize), 1, 0, nullptr, nextPowerOf2(vectorSize)); + encryptInn = context->EvalMult(encryptedInput, mask_data); + } + else{ + scaleValue = 1; + } + + Ctext relu_result = context->EvalChebyshevFunction( + [scaleValue](double x) -> double { if (x < 0) return 0; else return scaleValue*x; }, + encryptInn, + lowerBound, + upperBound, + polyDegree); + return relu_result; +} + +/** + * @brief Perform secure striding on encrypted data using a basic, low-noise approach. + * + * This function applies striding to a ciphertext representing an input feature map. + * It is designed to minimize noise growth in FHE computations, although it may + * be slower than optimized striding methods. This function is suitable for + * all operations requiring striding in encrypted neural network layers. + * + * @param in_cipher Encrypted input feature map (ciphertext). + * @param inputWidth Width of the input feature map (assumed square). + * @param widthOut Width of the output feature map after striding. + * @param stride stride length for the operation. + * + * @return Ctext Ciphertext representing the strided encrypted feature map. + * + * @see he_convolution() + * @see he_convolution_advanced() + */ +Ctext FHEONANNController::basic_striding(Ctext in_cipher, int inputWidth, int widthOut, int stride){ + + auto in_digits = context->EvalFastRotationPrecompute(in_cipher); + vector chan_vec(widthOut); + vector rotated_ciphertexts(widthOut); + int i_rot = stride*inputWidth; + + for (int k =0; kEvalFastRotation(in_cipher, i_rot, context->GetCyclotomicOrder(), in_digits); + in_digits = context->EvalFastRotationPrecompute(in_cipher); + } + for(int t=0; tEvalFastRotation(in_cipher, t*stride, context->GetCyclotomicOrder(), in_digits); + } + } + Ctext merged_cipher = context->EvalMerge(rotated_ciphertexts); + if(k == 0){ + chan_vec[k] = merged_cipher; + } + else{ + chan_vec[k] = context->EvalRotate(merged_cipher, -k*widthOut); + } + } + return context->EvalAddMany(chan_vec); +} + + +// Apply convolution with batch channel processing +Ctext FHEONANNController::batch_convolution_operation(const vector& rotatedInputs, const vector& kernelData, int kernelSq, int inputSize, int inputChannels) { + + // Apply kernel to each rotated cipher + vector kernelSum(kernelSq); + for (int j = 0; j < kernelSq; ++j) { + kernelSum[j] = context->EvalMult(rotatedInputs[j], kernelData[j]); + } + return context->EvalAddMany(kernelSum); +} + +/** + * @brief Perform secure downsampling (striding) on encrypted data over channels. + * + * This function applies striding to reduce the spatial resolution of an encrypted + * feature map, effectively performing downsampling. It is designed for use across + * all layers requiring striding in FHE-based neural network implementations. + * It allow downsampling over a single layer + * + * @param input Encrypted input feature map (ciphertext). + * @param inputWidth Width of the input feature map (assumed square). + * @param stride stride length used for downsampling. + * + * @return Ctext Ciphertext representing the downsampled feature map + * + * @see basic_striding() + * @see he_convolution() + * @see he_avgpool_advanced() + */ +Ctext FHEONANNController::downsample(const Ctext& input, int inputWidth, int stride) { + + Ctext result = input->Clone(); + const int outputWidth = inputWidth / stride; + const int inputSize = inputWidth * inputWidth; + + // Step 1: Binary decomposition for row juxtaposition + result = context->EvalMult(result, first_mask(inputWidth, inputSize, stride, input->GetLevel())); + for (int s = 1; s < log2(outputWidth); s++) { + result = context->EvalMult( + context->EvalAdd(result, context->EvalRotate(result, pow(2, s-1))), + generate_binary_mask(pow(2,s), inputSize, stride, input->GetLevel()) + ); + } + result = context->EvalAdd(result, context->EvalRotate(result, pow(2, (log2(outputWidth)-1)))); + // Step 2: Row processing with optimized rotations + Ctext downsampledrows = context->EvalMult(input, generate_zero_mask(inputSize, input->GetLevel())); + + for (int row = 0; row < outputWidth; ++row) { + Ctext masked = context->EvalMult(result, generate_row_mask(row, outputWidth, inputSize, stride, input->GetLevel())); + downsampledrows = context->EvalAdd(downsampledrows, masked); + if(row < outputWidth-1){ + result = context->EvalRotate(result, (stride*inputWidth - outputWidth)); + } + } + return downsampledrows; +} + +/** + * @brief Perform secure multi-channel downsampling (striding) on encrypted data. + * + * This function applies striding across multiple channels of an encrypted feature + * map to reduce its spatial resolution. It is optimized to handle all channels + * simultaneously, improving efficiency for FHE-based convolutional and pooling layers. + * + * @param input Encrypted input feature map (ciphertext). + * @param inputWidth Width of the input feature map (assumed square). + * @param stride stride length used for downsampling. + * @param numChannels Number of channels in the input feature map. + * + * @return Ctext Ciphertext representing the downsampled feature map across all channels. + * + * @see downsample() + * @see basic_striding() + * @see he_convolution() + */ +Ctext FHEONANNController::downsample_with_multiple_channels(const Ctext& input, int inputWidth, int stride, int numChannels) { + + const int inputSize = inputWidth * inputWidth; + const int outputWidth = inputWidth / stride; + const int level = input->GetLevel(); + int outputSize = outputWidth * outputWidth; + + Ctext encryptedzeros = context->EvalMult(input, generate_zero_mask_channels(inputSize, numChannels, input->GetLevel())); + + // 2) binary-row decomposition + Ctext result = context->EvalMult(input, first_mask_with_channels(inputWidth, inputSize, stride, numChannels, level)); + + // Step 1: Binary decomposition for row juxtaposition + for (int s = 1; s < log2(outputWidth); s++) { + result = context->EvalMult( + context->EvalAdd(result, context->EvalRotate(result, pow(2, s-1))), + generate_binary_mask_with_channels(pow(2,s), inputSize, stride, numChannels, input->GetLevel()) + ); + } + + result = context->EvalAdd(result, context->EvalRotate(result, pow(2, (log2(outputWidth)-1)))); + + // Step 2: Row processing with optimized rotations + Ctext downsampledrows = encryptedzeros->Clone(); + for (int row = 0; row < outputWidth; row++) { + Ctext masked = context->EvalMult(result, generate_row_mask_with_channels(row, outputWidth, inputSize, stride, numChannels, input->GetLevel())); + downsampledrows = context->EvalAdd(downsampledrows, masked); + if(row < outputWidth-1){ + result = context->EvalRotate(result, (stride*inputWidth - outputWidth)); + } + } + + /*** + * step 3: process per channel + ******/ + Ctext downsampledchannels = encryptedzeros->Clone(); + for (int ch=0; ch < numChannels; ch++){ + Ctext masked = context->EvalMult(downsampledrows, generate_channel_mask_with_zeros(ch, outputSize, numChannels, input->GetLevel())); + downsampledchannels = context->EvalAdd(downsampledchannels, masked); + if(ch < numChannels-1){ + downsampledrows = context->EvalRotate(downsampledrows, (inputSize - outputSize)); + } + } + + /*** step 3: process per channel */ + // int totalSize = numChannels * outputSize; + // Ctext downsampledchannels = encryptedzeros->Clone(); + // for (int i = 0; i < numChannels; i++) { + // Ctext masked = context->EvalMult(downsampledrows, gen_channel_full_mask(i, inputSize, outputSize, numChannels, downsampledrows->GetLevel())); + // downsampledchannels = context->EvalAdd(downsampledchannels, masked); + // downsampledchannels = context->EvalRotate(downsampledchannels, -(inputSize - outputSize)); + // } + + // downsampledchannels = context->EvalRotate(downsampledchannels, (inputSize - outputSize) * numChannels); + // downsampledchannels = context->EvalAdd(downsampledchannels, context->EvalRotate(downsampledchannels, -totalSize)); + + return downsampledchannels; +} + +/** + * @brief Generate a mask selecting the first strided elements in a single channel. + * + * @param width Width of the input. + * @param inputSize Total number of elements in the input. + * @param stride stride value for selecting elements. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with selected elements set to 1. + */ +Ptext FHEONANNController::first_mask(int width, int inputSize, int stride, int level) { + vector mask(inputSize, 0); + for (int i = 0; i < width; i++) { + for (int j = 0; j < width; j++) { + if (j % stride == 0 && i % stride == 0) { + int index = i * width + j; + mask[index] = 1.0; + } + } + } + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} + +/** + * @brief Generate a repeating binary mask pattern for a single channel. + * + * @param pattern Number of consecutive ones before inserting zeros. + * @param inputSize Total number of elements in the input. + * @param stride Unused here but kept for consistency. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with repeating binary pattern. + */ +Ptext FHEONANNController::generate_binary_mask(int pattern, int inputSize, int stride, int level) { + vector mask; + int copy_interval = pattern; + for (int i = 0; i < inputSize; i++) { + if (copy_interval > 0) { + mask.push_back(1); + } else { + mask.push_back(0); + } + + copy_interval--; + + if (copy_interval <= -pattern) { + copy_interval = pattern; + } + } + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} + +/** + * @brief Generate a mask selecting a specific row in a single channel. + * + * @param row Row index to select. + * @param width Width of the input. + * @param inputSize Total number of elements in the input. + * @param stride Unused here but kept for consistency. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with the specified row set to 1. + */ +Ptext FHEONANNController::generate_row_mask(int row, int width, int inputSize, int stride, int level) { + vector mask; + + for (int j = 0; j < (row * width); j++) { + mask.push_back(0); + } + for (int j = 0; j < width; j++) { + mask.push_back(1); + } + for (int j = 0; j < (inputSize - width - (row * width)); j++) { + mask.push_back(0); + } + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} + +/** + * @brief Generate a zero mask of given size. + * + * @param size Number of elements in the mask. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with all zeros. + */ +Ptext FHEONANNController::generate_zero_mask(int size, int level) { + vector mask(size, 0.0); + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} + +/** + * @brief Generate a mask selecting a block of a channel while zeroing other slots. + * + * @param n Channel/block index to select. + * @param in_elements Number of elements per channel. + * @param out_elements Number of elements to select in the block. + * @param numChannels Total number of channels. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with the selected block set to 1. + */ +Ptext FHEONANNController::generate_channel_full_mask(int n, int in_elements, int out_elements, int numChannels, int level) { + + const int totalSlots = in_elements * numChannels; + std::vector mask(totalSlots, 0.0); + const int base = n * in_elements; + for (int i = 0; i < out_elements; ++i){ + mask[base + i] = 1.0; + } + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} + +/** + * @brief Generate a zero mask across all channels. + * + * @param inputSize Number of elements per channel. + * @param numChannels Total number of channels. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with all zeros. + */ +Ptext FHEONANNController::generate_zero_mask_channels(int inputSize, int numChannels, int level) { + + int totalSlots = inputSize * numChannels; + vector mask(totalSlots, 0.0); + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} + +/** + * @brief Generate a mask selecting the first strided row in every channel. + * + * @param inputWidth Width of each channel. + * @param inputSize Total number of elements per channel. + * @param stride stride value for selecting elements. + * @param numChannels Total number of channels. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with selected elements in all channels. + */ +Ptext FHEONANNController::first_mask_with_channels(int inputWidth, int inputSize, int stride, int numChannels, int level) { + // int outputWidth = inputWidth / stride; + vector mask; + vector baseMask(inputSize, 0.0); + for (int i = 0; i < inputWidth; i++) { + for (int j = 0; j < inputWidth; j++) { + if (j % stride == 0 && i % stride == 0) { + int index =(i * inputWidth + j); + baseMask[index] = 1.0; + } + } + } + + for (int ch = 0; ch < numChannels; ch++) { + mask.insert(mask.end(), baseMask.begin(), baseMask.end()); + } + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} + +/** + * @brief Generate a repeating binary mask across all channels. + * + * @param pattern Number of consecutive ones before zeros. + * @param inputSize Number of elements per channel. + * @param stride Unused here but kept for consistency. + * @param numChannels Total number of channels. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with repeated binary pattern across channels. + */ +Ptext FHEONANNController::generate_binary_mask_with_channels(int pattern, int inputSize, int stride, int numChannels, int level) { + + vector baseMask; + int copy_interval = pattern; + for (int i = 0; i < inputSize; i++) { + if (copy_interval > 0) { + baseMask.push_back(1); + } else { + baseMask.push_back(0); + } + + copy_interval--; + + if (copy_interval <= -pattern) { + copy_interval = pattern; + } + } + + // repeat baseMask n times + vector mask; + mask.reserve(baseMask.size() * numChannels); + for (int i = 0; i < numChannels; i++) { + mask.insert(mask.end(), baseMask.begin(), baseMask.end()); + } + + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} + +/** + * @brief Generate a mask selecting a specific row in every channel. + * + * @param row Row index to select. + * @param width Width of each channel. + * @param inputSize Number of elements per channel. + * @param stride Unused here but kept for consistency. + * @param numChannels Total number of channels. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with the row selected in all channels. + */ +Ptext FHEONANNController::generate_row_mask_with_channels(int row, int width, int inputSize, int stride, int numChannels, int level) { + + vector baseMask; + for (int j = 0; j < (row * width); j++) { + baseMask.push_back(0); + } + for (int j = 0; j < width; j++) { + baseMask.push_back(1); + } + for (int j = 0; j < (inputSize - width - (row * width)); j++) { + baseMask.push_back(0); + } + + // repeat baseMask n times + vector mask; + mask.reserve(baseMask.size() * numChannels); + for (int i = 0; i < numChannels; i++) { + mask.insert(mask.end(), baseMask.begin(), baseMask.end()); + } + + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} + +/** + * @brief Generate a mask selecting a specific channel while zeroing all others. + * + * @param channel Channel index to select. + * @param outputSize Number of elements per channel. + * @param numChannels Total number of channels. + * @param level Encryption level for CKKS plaintext. + * @return Packed plaintext mask with the selected channel set to 1. + */ +Ptext FHEONANNController::generate_channel_mask_with_zeros(int channel, int outputSize, int numChannels, int level ){ + + int totalSlots = outputSize * numChannels; + vector mask(totalSlots, 0.0); + + int pos = channel * outputSize; + for (int i = 0; i < outputSize; i++) { + mask[pos + i] = 1.0; + } + return context->MakeCKKSPackedPlaintext(mask, 1.0, level); +} diff --git a/submissions/cifar10/fheon/FHEONHEController.cpp b/submissions/cifar10/fheon/FHEONHEController.cpp new file mode 100755 index 0000000..6abbd06 --- /dev/null +++ b/submissions/cifar10/fheon/FHEONHEController.cpp @@ -0,0 +1,1466 @@ + +/*********************************************************************************************************************** +* +* @author: Nges Brian, Njungle +* +* MIT License +* Copyright (c) 2025 Secure, Trusted and Assured Microelectronics, Arizona State University + +* Permission is hereby granted, free of charge, to any person obtaining a copy +* of this software and associated documentation files (the "Software"), to deal +* in the Software without restriction, including without limitation the rights +* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +* copies of the Software, and to permit persons to whom the Software is +* furnished to do so, subject to the following conditions: + +* The above copyright notice and this permission notice shall be included in all +* copies or substantial portions of the Software. + +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +********************************************************************************************************************/ + +/** + * @brief FHE controller for defining basic FHE functions used across different neural networks. + * + * This class provides fundamental methods for context generation, encryption, encoding, + * bootstrapping, and other FHE operations that are utilized throughout the ANN development. + * + */ + +#include +#include +#include +// #include +namespace fs = std::filesystem; + +#include "FHEONHEController.h" + +/** + * @brief Compute the PQ value, which defines the application's security level. + * + * This function calculates the PQ value from the given polynomial, which is used + * to determine the security level of the application. + * + * @param poly Input polynomial used to compute the PQ value. + * + * @return The computed PQ value as a double. + */ + +double FHEONHEController::getlogPQ(const DCRTPoly &poly) { + int n = poly.GetNumOfElements(); + double logPQ = 0; + for (int i = 0; i < n; i++) { + auto qi = poly.GetParams()->GetParams()[i]->GetModulus(); + logPQ += log(qi.ConvertToDouble()) / log(2); + } + return logPQ; +} + + +/** + * @brief Generate the full FHE context for the project. + * + * This function sets up the FHE context with all specified parameters, allowing + * fine-grained control over scaling, decomposition, and level budgets. Keys can + * be optionally serialized and saved. + * + * @param ringDim Ring dimension. + * @param numSlots Number of slots (batch size). + * @param mlevelBootstrap Multiplication level after bootstrapping. + * @param dcrtBits Scaling factor for DCRT representation. + * @param firstMod Scaling factor for the first coefficients. + * @param numDigits Number of digits used in key decomposition. + * @param levelBudget Vector of budget levels. + * @param serialize Whether to serialize and save keys. + */ +void FHEONHEController::generate_context(int ringDim, int numSlots, int mlevelBootstrap, + int dcrtBits, int firstMod, int numDigits, vector levelBudget, + bool serialize) { + + CCParams parameters; + auto secretKeyDist = SPARSE_TERNARY; + + ScalingTechnique rescaleTech = FLEXIBLEAUTO; + level_budget = levelBudget; + num_slots = 1 << numSlots; + mult_depth = mlevelBootstrap; + + parameters.SetRingDim(1 << ringDim); + parameters.SetBatchSize(num_slots); + parameters.SetScalingModSize(dcrtBits); + parameters.SetFirstModSize(firstMod); + parameters.SetNumLargeDigits(numDigits); + + parameters.SetSecretKeyDist(secretKeyDist); + parameters.SetSecurityLevel(lbcrypto::HEStd_NotSet); + // parameters.SetSecurityLevel(lbcrypto::HEStd_128_classic); + parameters.SetScalingTechnique(rescaleTech); + + circuit_depth = mult_depth + FHECKKSRNS::GetBootstrapDepth(level_budget, secretKeyDist); + parameters.SetMultiplicativeDepth(circuit_depth); + + cout << "Building the FHE Context" << endl; + cout << "dcrtBits: "<< dcrtBits << " -- firstMod: " << firstMod << endl << "Ciphertexts depth: " + << circuit_depth << ", available multiplications: " << circuit_depth - 2 << endl; + + context = GenCryptoContext(parameters); + context->Enable(PKE); + context->Enable(KEYSWITCH); + context->Enable(LEVELEDSHE); + context->Enable(ADVANCEDSHE); + context->Enable(FHE); + + keyPair = context->KeyGen(); + context->EvalMultKeyGen(keyPair.secretKey); + context->EvalSumKeyGen(keyPair.secretKey); + + ringDim = context->GetRingDimension(); + numSlots = num_slots; + usint halfnumSlots = numSlots/2; + context->EvalBootstrapSetup(level_budget, bsgsDim, numSlots); + context->EvalBootstrapKeyGen(keyPair.secretKey, numSlots); + + auto sec_level = parameters.GetSecurityLevel(); + auto logq = context->GetModulus().GetMSB(); + double logPQ = getlogPQ(keyPair.publicKey->GetPublicElements()[0]); + cout << "Keys Generated." << endl; + cout << "Cyclotomic Order: " << context->GetCyclotomicOrder() << endl; + cout << "CKKS scheme is using ring dimension: " << ringDim << endl; + cout << "Avaliable numSlots: " << numSlots << " - halfnumSlots: " << halfnumSlots << endl; + cout << "LogQ: "<< logq << " - Security Level: " << endl; + cout << "Security Level: " << sec_level << endl; + cout << "Ciphertexts depth: " << circuit_depth << endl; + cout << "Multiplication Depth: " << mult_depth - 2 << endl; + cout << "log PQ = " << logPQ << std::endl << std::endl; + cout << "-----------------------------------------------------------" << endl; + + if(serialize){ + write_to_file(keys_folder + "/mult_depth.txt", to_string(mult_depth)); + write_to_file(keys_folder + "/num_slots.txt", to_string(num_slots)); + write_to_file(keys_folder + "/level_budget.txt", to_string(level_budget[0]) + "," + to_string(level_budget[1])); + keys_serialization(); + } + + + #include +#include + + + return; +} + +/** + * @brief Simplified version of generate_context using standard values for unspecified parameters. + * + * This function sets up the FHE context with default parameters for all unspecified + * values, simplifying context generation for typical use cases. + * + * @param ringDim Ring dimension. + * @param numSlots Number of slots (batch size). + * @param mlevelBootstrap Multiplication level after bootstrapping. + * @param serialize Whether to serialize and save keys. + */ +void FHEONHEController::generate_context(int ringDim, int numSlots, int mlevelBootstrap, bool serialize) { + CCParams parameters; + + num_slots = 1 << numSlots; + int dcrtBits = 46; + int firstMod = 50; + + auto secretKeyDist = SPARSE_TERNARY; + parameters.SetSecretKeyDist(secretKeyDist); + parameters.SetSecurityLevel(lbcrypto::HEStd_NotSet); + parameters.SetNumLargeDigits(3); + parameters.SetRingDim(1 << ringDim); + parameters.SetBatchSize(num_slots); + ScalingTechnique rescaleTech = FLEXIBLEAUTO; + parameters.SetScalingModSize(dcrtBits); + parameters.SetFirstModSize(firstMod); + parameters.SetScalingTechnique(rescaleTech); + mult_depth = mlevelBootstrap; + uint32_t levelsAvailableAfterBootstrap = mult_depth; + + circuit_depth = levelsAvailableAfterBootstrap + FHECKKSRNS::GetBootstrapDepth(level_budget, secretKeyDist); + + cout << "Context built, generating keys..." << endl; + cout << endl << "dcrtBits: "<< dcrtBits << " -- firstMod: " << firstMod << endl << "Ciphertexts depth: " + << circuit_depth << ", available multiplications: " << levelsAvailableAfterBootstrap - 2 << endl; + + parameters.SetMultiplicativeDepth(circuit_depth); + context = GenCryptoContext(parameters); + + context->Enable(PKE); + context->Enable(KEYSWITCH); + context->Enable(LEVELEDSHE); + context->Enable(ADVANCEDSHE); + context->Enable(FHE); + + keyPair = context->KeyGen(); + context->EvalMultKeyGen(keyPair.secretKey); + context->EvalSumKeyGen(keyPair.secretKey); + + numSlots = num_slots; + usint halfnumSlots = numSlots/2; + cout << "numSlots: " << numSlots << " - halfnumSlots: " << halfnumSlots << endl; + context->EvalBootstrapSetup(level_budget, bsgsDim, numSlots); + context->EvalBootstrapKeyGen(keyPair.secretKey, numSlots); + + cout << " Keys Generated." << endl; + ringDim = context->GetRingDimension(); + cout << " CKKS scheme is using ring dimension: " << ringDim << endl; + cout << " Ciphertexts depth: " << circuit_depth << endl; + cout << " Multiplication Depth: " << levelsAvailableAfterBootstrap - 2 << endl; + cout << " Cyclotomic Order: " << context->GetCyclotomicOrder() << endl; + cout << " -----------------------------------------------------------" << endl; + + if(serialize){ + write_to_file(keys_folder + "/mult_depth.txt", to_string(mult_depth)); + write_to_file(keys_folder + "/level_budget.txt", to_string(level_budget[0]) + "," + to_string(level_budget[1])); + keys_serialization(); + } + return; +} + + +/** + * @brief Generate all evaluation keys and save them to the keys folder. + * + * This function generates all necessary evaluation keys for the FHE context + * and serializes them into the designated keys folder for later use. + */ + +void FHEONHEController::keys_serialization(){ + + cout << "------------------------------------------------------------" << endl; + cout << "Now serializing keys ..." << endl; + + if (!fs::exists(keys_folder)) { + if (!fs::create_directory(keys_folder)) { + std::cerr << "Failed to create directory: " << keys_folder << std::endl; + return; + } + } + + if (!Serial::SerializeToFile(keys_folder + "/crypto-context.bin", context, SerType::BINARY)) { + cerr << "Error writing serialization of the crypto context to crypto-context.bin" << endl; + } else { + cout << "Crypto Context have been serialized" << std::endl; + } + + ofstream multKeyFile(keys_folder + "/mult-keys.bin", ios::out | ios::binary); + if (multKeyFile.is_open()) { + if (!context->SerializeEvalMultKey(multKeyFile, SerType::BINARY)) { + cerr << "Error writing eval mult keys" << std::endl; + exit(1); + } + cout << "Relinearization Keys have been serialized" << std::endl; + multKeyFile.close(); + } + else { + cerr << "Error serializing EvalMult keys in \"" << keys_folder + "/mult-keys.bin" << "\"" << endl; + exit(1); + } + + ofstream sumKeysFile(keys_folder + "/sum-keys.bin", ios::out | ios::binary); + if (sumKeysFile.is_open()) { + if (!context->SerializeEvalSumKey(sumKeysFile, SerType::BINARY)) { + cerr << "Error writing sum keys" << std::endl; + exit(1); + } + cout << "sum keys have been serialized" << std::endl; + } else { + cerr << "Error serializing sum keys \"" << keys_folder + "/sum-keys" << "\"" << std::endl; + exit(1); + } + + if (!Serial::SerializeToFile(keys_folder + "/public-key.bin", keyPair.publicKey, SerType::BINARY)) { + cerr << "Error writing serialization of public key to public-key.bin" << endl; + } else { + cout << "Public Key has been serialized" << std::endl; + } + + if (!Serial::SerializeToFile(keys_folder + "/secret-key.bin", keyPair.secretKey, SerType::BINARY)) { + cerr << "Error writing serialization of public key to secret-key.bin" << endl; + } else { + cout << "Secret Key has been serialized" << std::endl; + } + return; +} + +/** + * @brief Load all serialized keys from the storage folder. + * + * This function reads and loads all keys that were previously serialized + * and stored in files, typically from the "sskeys" folder. + */ +void FHEONHEController::load_context(bool verbose) { + + context->ClearEvalMultKeys(); + context->ClearEvalAutomorphismKeys(); + CryptoContextFactory::ReleaseAllContexts(); + + cout << "------------------------------------------------------------" << endl; + if (verbose) cout << "Reading serialized context..." << endl; + + if (!Serial::DeserializeFromFile(keys_folder + "/crypto-context.bin", context, SerType::BINARY)) { + cerr << "I cannot read serialized data from: " << keys_folder + "/crypto-context.bin" << endl; + exit(1); + } + + PublicKey clientPublicKey; + if (!Serial::DeserializeFromFile(keys_folder + "/public-key.bin", clientPublicKey, SerType::BINARY)) { + cerr << "I cannot read serialized data from public-key.bin" << endl; + exit(1); + } + + PrivateKey serverSecretKey; + if (!Serial::DeserializeFromFile(keys_folder + "/secret-key.bin", serverSecretKey, SerType::BINARY)) { + cerr << "I cannot read serialized data from secret-key.bin" << endl; + exit(1); + } + + keyPair.publicKey = clientPublicKey; + keyPair.secretKey = serverSecretKey; + + std::ifstream multKeyIStream(keys_folder + "/mult-keys.bin", ios::in | ios::binary); + if (!multKeyIStream.is_open()) { + cerr << "Cannot read serialization from " << "mult-keys.bin" << endl; + exit(1); + } + if (!context->DeserializeEvalMultKey(multKeyIStream, SerType::BINARY)) { + cerr << "Could not deserialize eval multkey file" << endl; + exit(1); + } + + ifstream sumKeyIStream(keys_folder + "/sum-keys.bin", ios::in | ios::binary); + if (!sumKeyIStream.is_open()) { + cerr << "Cannot read serialization from " << "sum-keys.bin" << std::endl; + exit(1); + } + if (!context->DeserializeEvalSumKey(sumKeyIStream, SerType::BINARY)) { + cerr << "Could not deserialize eval rot key file" << std::endl; + exit(1); + } + + mult_depth = stoi(read_from_file(keys_folder + "/mult_depth.txt")); + level_budget[0] = read_from_file(keys_folder + "/level_budget.txt").at(0) - '0'; + level_budget[1] = read_from_file(keys_folder + "/level_budget.txt").at(2) - '0'; + + uint32_t approxBootstrapDepth = 4 + 4; + uint32_t levelsUsedBeforeBootstrap = mult_depth; + circuit_depth = levelsUsedBeforeBootstrap + FHECKKSRNS::GetBootstrapDepth(approxBootstrapDepth, level_budget, SPARSE_TERNARY); + + if (verbose) cout << "Circuit depth: " << circuit_depth << ", available multiplications: " << levelsUsedBeforeBootstrap - 2 << endl; + + cout << "Context Loaded" << endl; + cout << "------------------------------------------------------------" << endl; +} + +/** + * @brief Generate the bootstrapping keys for the FHE context. + * + * This function generates bootstrapping keys for the specified number of slots. + * The generated keys can be optionally serialized and saved to a file. + * + * @param bootstrap_slots Number of bootstrapping slots. + * @param filename Filename to use when saving the keys. + * @param serialize Whether to serialize and save the bootstrapping keys. + */ +void FHEONHEController::generate_bootstrapping_keys(int bootstrap_slots, string filename, bool serialize) { + + // int numSlots = 1<EvalBootstrapSetup(level_budget, bsgsDim, numSlots); + // context->EvalBootstrapKeyGen(keyPair.secretKey, numSlots); + context->EvalMultKeyGen(keyPair.secretKey); + + if(serialize){ + ofstream multKeysFile(keys_folder + mult_prefix + filename, ios::out | ios::binary); + if (multKeysFile.is_open()) { + if (!context->SerializeEvalMultKey(multKeysFile, SerType::BINARY)) { + cerr << "Error writing mult keys" << std::endl; + exit(1); + } + cout << "mult keys \"" << filename << "\" have been serialized" << std::endl; + } else { + cerr << "Error serializing mult keys" << keys_folder + mult_prefix + filename << std::endl; + exit(1); + } + } +} + + +/** + * @brief Generate and serialize rotation keys for the FHE context. + * + * This function generates rotation keys for the specified rotation positions. + * The generated keys can be optionally serialized and saved to a file. + * + * @param rotations Vector of rotation positions to generate keys for. + * @param filename Filename to use when saving the rotation keys. + * @param serialize Whether to serialize and save the rotation keys. + */ +void FHEONHEController::generate_rotation_keys(const vector rotations, std::string filename, bool serialize) { + + if (serialize && filename.size() == 0) { + cout << "Filename cannot be empty when serializing rotation keys." << endl; + return; + } + context->EvalRotateKeyGen(keyPair.secretKey, rotations); + if (serialize) { + ofstream rotationKeyFile(keys_folder + rotation_prefix + filename, ios::out | ios::binary); + if (rotationKeyFile.is_open()) { + if (!context->SerializeEvalAutomorphismKey(rotationKeyFile, SerType::BINARY)) { + cerr << "Error writing rotation keys" << std::endl; + exit(1); + } + cout << "Rotation keys \"" << filename << "\" have been serialized" << std::endl; + } else { + cerr << "Error serializing Rotation keys" << keys_folder + rotation_prefix + filename << std::endl; + exit(1); + } + } +} + + +/** + * @brief Generate and serialize both rotation keys and bootstrapping keys for the FHE context. + * + * This function generates rotation keys for the specified rotation positions and + * bootstrapping keys for the given number of slots. The generated keys can be + * optionally serialized and saved to a file. + * + * @param rotations Vector of rotation positions to generate keys for. + * @param bootstrap_slots Number of bootstrapping slots. + * @param filename Filename to use when saving the keys. + * @param serialize Whether to serialize and save the generated keys. + */ +void FHEONHEController::generate_bootstrapping_and_rotation_keys(vector rotations, int bootstrap_slots, const string& filename, bool serialize) { + if (serialize && filename.empty()) { + cout << "Filename cannot be empty when serializing bootstrapping and rotation keys." << endl; + return; + } + + generate_bootstrapping_keys(bootstrap_slots, filename, serialize); + generate_rotation_keys(rotations, filename, serialize); +} + + +/** + * @brief Load previously generated bootstrapping and rotation keys from storage. + * + * This function loads bootstrapping and rotation keys that were previously + * generated and serialized, using the specified filename. Verbose mode can + * be enabled to display loading details. + * + * @param bootstrap_slots Number of bootstrapping slots. + * @param filename Filename from which to load the keys. + * @param verbose Whether to display detailed loading information. + */ +void FHEONHEController::load_bootstrapping_and_rotation_keys(int bootstrap_slots, const string& filename, bool verbose) { + if (verbose) cout << endl << "Loading bootstrapping and rotations keys from " << filename << "..." << endl; + + int numSlots = 1 << bootstrap_slots; + context->EvalBootstrapSetup(level_budget, bsgsDim, numSlots); + context->EvalBootstrapKeyGen(keyPair.secretKey, numSlots); + + if (verbose) cout << "(1/4) Bootstrapping precomputations completed!" << endl; + + ifstream multKeyIStream(keys_folder + mult_prefix + filename, ios::in | ios::binary); + if (!multKeyIStream.is_open()) { + cerr << "Cannot read serialization from " << keys_folder+ "/" << mult_prefix << filename << std::endl; + exit(1); + } + if (!context->DeserializeEvalMultKey(multKeyIStream, SerType::BINARY)) { + cerr << "Could not deserialize eval rot key file" << std::endl; + exit(1); + } + if (verbose) cout << "(2/4) MultKey deserialized and loaded!" << endl; + + ifstream rotKeyIStream(keys_folder + rotation_prefix + filename, ios::in | ios::binary); + if (!rotKeyIStream.is_open()) { + cerr << "Cannot read serialization from " << keys_folder+ "/" << rotation_prefix << filename << std::endl; + exit(1); + } + if (!context->DeserializeEvalAutomorphismKey(rotKeyIStream, SerType::BINARY)) { + cerr << "Could not deserialize eval rot key file" << std::endl; + exit(1); + } + if (verbose) cout << "(4/4) Rotation keys deserialized and loaded!" << endl; + if (verbose) cout << endl; +} + + +/** + * @brief Load rotation keys from a specified file. + * + * This function loads rotation keys that were previously generated and serialized + * from the given filename. Verbose mode can be enabled to display loading details. + * + * @param filename Filename from which to load the rotation keys. + * @param verbose Whether to display detailed loading information. + */ +void FHEONHEController::load_rotation_keys(const string& filename, bool verbose) { + + if (verbose) cout << endl << "Loading rotations keys from " << filename << "..." << endl; + + ifstream rotKeyIStream(keys_folder + rotation_prefix + filename, ios::in | ios::binary); + if (!rotKeyIStream.is_open()) { + cerr << "Cannot read serialization from " <DeserializeEvalAutomorphismKey(rotKeyIStream, SerType::BINARY)) { + cerr << "Could not deserialize eval rot key file" << std::endl; + exit(1); + } + + if (verbose) { + cout << "(1/1) Rotation keys read!" << endl; + cout << endl; + } +} + +/** + * @brief Clear all rotation keys stored in the FHE context. + * + * This function removes all previously stored rotation keys from the context, + * allowing new rotation keys to be generated without conflicts. + */ +void FHEONHEController::clear_rotation_keys() { + context->ClearEvalMultKeys(); + context->ClearEvalAutomorphismKeys(); + CryptoContextFactory::ReleaseAllContexts(); +} + +/** + * @brief Clear all bootstrapping and rotation keys in the FHE context. + * + * This function removes all stored bootstrapping and rotation keys up to the + * specified number of bootstrapping slots, allowing new keys to be generated. + * + * @param bootstrap_num_slots Number of bootstrapping slots to clear. + */ +void FHEONHEController::clear_bootstrapping_and_rotation_keys(int bootstrap_num_slots) { + //This lines would free more or less 1GB or precomputations, but requires access to the GetFHE function + // FHECKKSRNS* derivedPtr = dynamic_cast(context->GetScheme()->GetFHE().get()); + // derivedPtr->m_bootPrecomMap.erase(bootstrap_num_slots); + + context->ClearEvalMultKeys(); + context->ClearEvalAutomorphismKeys(); + CryptoContextFactory::ReleaseAllContexts(); +} + + +/** + * @brief Clear the entire FHE context, including multiplication, bootstrapping, and rotation keys. + * + * This function removes all keys stored in the context, allowing a fresh setup + * or reinitialization of the FHE environment. + * + * @param bootstrapping_key_slots Number of bootstrapping slots to clear. + */ +void FHEONHEController::clear_context(int bootstrapping_key_slots) { + + if (bootstrapping_key_slots != 0) + clear_bootstrapping_and_rotation_keys(bootstrapping_key_slots); + else + clear_rotation_keys(); +} + + +/** + * @brief Bootstrap a ciphertext to refresh its noise budget. + * + * This function applies bootstrapping to the input ciphertext, effectively + * reducing accumulated noise and enabling further homomorphic operations. + * The bootstrapping level controls the depth and parameters used. + * + * @param encryptedInput Ciphertext to be bootstrapped. + * @param encode_level Bootstrapping level as defined in OpenFHE (e.g., 1 or 2). + * + * @return Refreshed ciphertext after bootstrapping. + */ +Ctext FHEONHEController::bootstrap_function(Ctext& encryptedInput, int encode_level){ + Ctext boots_ciphertext = context->EvalBootstrap(encryptedInput, encode_level); + return boots_ciphertext; +} + +/** + * @brief Bootstrap batched ciphertext to refresh the noise budget in all of them. + * + * This function applies bootstrapping to the input ciphertexts over the batch, effectively + * reducing accumulated noise and enabling further homomorphic operations. + * The bootstrapping level controls the depth and parameters used. + * + * @param encryptedInput Ciphertext to be bootstrapped. + * @param encode_level Bootstrapping level as defined in OpenFHE (e.g., 1 or 2). + * + * @return Refreshed ciphertext after bootstrapping. + */ + +// vector FHEONHEController::batch_bootstrap_function(vector& encryptedInputs, int inputChannels, int encode_level){ +// vector batch_ciphertexts(inputChannels); +// for(int b=0; bEvalBootstrap(encryptedInputs[b], encode_level); +// } +// return batch_ciphertexts; +// } + +vector FHEONHEController::batch_bootstrap_function(vector& encryptedInputs, int inputChannels, int encode_level) { + vector batch_ciphertexts(inputChannels); + int numThreads = min(inputChannels, (int)thread::hardware_concurrency()); + vector threads(numThreads); + + // Worker function for a range of channels + auto worker = [&](int start, int end) { + for (int b = start; b < end; b++) { + batch_ciphertexts[b] = context->EvalBootstrap(encryptedInputs[b], encode_level); + } + }; + + // Divide channels evenly among threads + int block = (inputChannels + numThreads - 1) / numThreads; + for (int t = 0; t < numThreads; t++) { + int start = t * block; + int end = min(start + block, inputChannels); + threads[t] = thread(worker, start, end); + } + for (auto &th : threads) th.join(); + + return batch_ciphertexts; +} + +/** + * @brief Encrypt a vector of input data into a packed ciphertext. + * + * This function takes a vector of double-precision values and encrypts them + * into a single packed ciphertext suitable for homomorphic computations. + * + * @param inputData Vector of data to be encrypted. + * + * @return Ciphertext containing the encrypted input data. + */ +Ctext FHEONHEController::encrypt_input(vector& inputData) { + Ptext plaintext = context->MakeCKKSPackedPlaintext(inputData, 1, 1); + plaintext->SetLength(inputData.size()); + auto encryptImage = context->Encrypt(keyPair.publicKey, plaintext); + return encryptImage; +} + + +/** + * @brief Encode a vector of input data into a packed plaintext. + * + * This function encodes a vector of double-precision values into a plaintext + * suitable for homomorphic encryption, using the specified encoding level. + * + * @param inputData Vector of data to be encoded. + * @param encode_level Encoding level to use for the plaintext. + * + * @return Plaintext containing the encoded input data. + */ +Ptext FHEONHEController::encode_input(vector& inputData, int encode_level) { + Ptext plaintext = context->MakeCKKSPackedPlaintext(inputData, 1, encode_level); + return plaintext; +} + +/** + * @brief Encode a vector of input data into a packed plaintext with a specified number of slots. + * + * This function encodes a vector of double-precision values into a plaintext + * suitable for homomorphic encryption, using the specified number of slots + * and encoding level. + * + * @param inputData Vector of data to be encoded. + * @param num_slots Number of slots to use in the plaintext. + * @param encode_level Encoding level to use for the plaintext. + * + * @return Plaintext containing the encoded input data. + */ + +Ptext FHEONHEController::encode_input(vector& inputData, int num_slots, int encode_level) { + int numElements = nextPowerOf2(num_slots); + Ptext plaintext = context->MakeCKKSPackedPlaintext(inputData, 1, encode_level, nullptr, numElements); + return plaintext; +} + +/** + * @brief Encode a vector for use in a shortcut layer. + * + * This function encodes a vector of double-precision values into a plaintext + * suitable for the shortcut layer in homomorphic neural network operations, + * using the specified column square size. + * + * @param inputData Vector of data to be encoded. + * @param cols_square Size of the column square for the encoding. + * + * @return Plaintext containing the encoded shortcut kernel data. + */ +Ptext FHEONHEController::encode_shortcut_kernel(vector& inputData, int cols_square) { + int dim1 = inputData.size(); + vector main_kernel; + for(int t =0; t< dim1; t++){ + double cell_value = inputData[t]; + vector repeated(cols_square, cell_value); + main_kernel.insert(main_kernel.end(), repeated.begin(), repeated.end()); + } + Ptext plaintext = context->MakeCKKSPackedPlaintext(main_kernel, 1, 1); + return plaintext; +} + +/** + * @brief Encode a vector of bias data into a plaintext. + * + * This function encodes a vector of double-precision bias values into a + * plaintext suitable for homomorphic neural network operations, using the + * specified column square size and encoding level. + * + * @param inputData Vector of bias data to be encoded. + * @param cols_square Size of the column square for the encoding. + * @param encode_level Encoding level to use for the plaintext. + * + * @return Plaintext containing the encoded bias data. + */ +Ptext FHEONHEController::encode_bais_input(vector& inputData, int cols_square, int encode_level) { + int dim1 = inputData.size(); + vector main_kernel; + for(int t =0; t< dim1; t++){ + double cell_value = inputData[t]; + vector repeated(cols_square, cell_value); + main_kernel.insert(main_kernel.end(), repeated.begin(), repeated.end()); + } + + Ptext plaintext = context->MakeCKKSPackedPlaintext(main_kernel, 1, encode_level); + return plaintext; +} + +/** + * @brief Re-encrypt a plaintext vector into a ciphertext. + * + * This function takes a plaintext containing encoded data and encrypts it + * into a ciphertext suitable for homomorphic computations. + * + * @param plaintextData Plaintext data to be re-encrypted. + * + * @return Ciphertext containing the encrypted data. + */ +Ctext FHEONHEController::reencrypt_data(Ptext plaintextData) { + + auto encryptedData = context->Encrypt(keyPair.publicKey, plaintextData); + return encryptedData; +} + +/** + * @brief Decrypt a ciphertext into a plaintext vector. + * + * This function takes an encrypted ciphertext and decrypts it into a plaintext + * vector suitable for further processing or inspection. + * + * @param encryptedinputData Ciphertext to be decrypted. + * @param cols Number of elements in the decrypted vector. + * + * @return Plaintext containing the decrypted data. + */ +Ptext FHEONHEController::decrypt_data(Ctext encryptedinputData, int cols) { + + Ptext plaintextDec; + context->Decrypt(keyPair.secretKey, encryptedinputData, &plaintextDec); + plaintextDec->SetLength(cols); + return plaintextDec; +} + +/** + * @brief Encrypt a 3D kernel matrix into a 2D vector of ciphertexts. + * + * This function takes a 3D vector of double-precision kernel values and encrypts + * each 2D slice into a vector of ciphertexts suitable for homomorphic convolution + * operations. + * + * @param kernelData 3D vector containing kernel values to be encrypted. + * @param cols_square Size of the column square for the encryption. + * + * @return 2D vector of ciphertexts representing the encrypted kernel. + */ +vector> FHEONHEController::encrypt_kernel(vector>>& kernelData, int cols_square){ + size_t dim1 = kernelData.size(); + if (dim1 == 0) return {}; + size_t dim2 = kernelData[0].size(); + if (dim2 == 0) return {}; + size_t dim3 = kernelData[0][0].size(); + if (dim3 == 0) return {}; + + vector> encrypt_kernel; + for (size_t k=0; k< dim1; k++){ + vector filters; + for (size_t i=0; i< dim2; i++){ + for (size_t j=0; j< dim3 ; j++){ + double cell_value = kernelData[k][i][j]; + vector repeated(cols_square, cell_value); + Ctext encrypted_val = encrypt_input(repeated); + filters.push_back(encrypted_val); + } + } + encrypt_kernel.push_back(filters); + } + return encrypt_kernel; +} + +/** + * @brief Encode kernel data for fully connected layers. + * + * This function takes a vector of double-precision kernel values and encodes + * them into plaintexts suitable for homomorphic operations in fully connected layers. + * + * @param kernelData Vector containing kernel values to be encoded. + * @param cols_square Size of the column square for the encoding. + * + * @return Vector of plaintexts representing the encoded kernel data. + */ +vector FHEONHEController::encode_kernel(vector& kernelData, int cols_square){ + size_t dim1 = kernelData.size(); + if (dim1 == 0) return {}; + + vector encrypt_kernel; + for (size_t j=0; j< dim1 ; j++){ + double cell_value = kernelData[j]; + vector repeated(cols_square, cell_value); + Ptext encrypted_val = encode_input(repeated); + encrypt_kernel.push_back(encrypted_val); + } + return encrypt_kernel; +} + +/** + * @brief Encode and replicate kernel values for homomorphic convolution. + * + * This function selects values from the kernel positions for all kernels, + * repeats them by the square of the width, concatenates them into a single + * long vector, and encodes the result. The output is a k^2 vector of repeated + * kernel values suitable for homomorphic convolution operations. + * + * @param kernelData Vector containing kernel values to be encoded and replicated. + * @param cols_square Size of the column square for encoding. + * + * @return Vector of plaintexts representing the encoded and replicated kernel values. + */ +vector FHEONHEController::encode_kernel(vector>>& kernelData, int cols_square){ + size_t dim1 = kernelData.size(); + if (dim1 == 0) return {}; + size_t dim2 = kernelData[0].size(); + if (dim2 == 0) return {}; + size_t dim3 = kernelData[0][0].size(); + if (dim3 == 0) return {}; + // cout <<"input kernel shape: " << dim1 << "*" << dim2 << "*" << dim3 <> main_kernel(kernelWidth_sq, vector()); + for (size_t k=0; k< dim1; k++){ + vector> filters; + for (size_t i=0; i< dim2; i++){ + for (size_t j=0; j< dim3 ; j++){ + double cell_value = kernelData[k][i][j]; + vector repeated(cols_square, cell_value); + filters.push_back(repeated); + } + } + for(int t =0; t< kernelWidth_sq; t++){ + main_kernel[t].insert(main_kernel[t].end(), filters[t].begin(), filters[t].end()); + } + } + vector encoded_kernel; + for( int s =0; s< kernelWidth_sq; s++){ + // cout << "Kernel size: " << main_kernel[s].size() << endl; + Ptext encoded_val = encode_input(main_kernel[s]); + encoded_kernel.push_back(encoded_val); + } + return encoded_kernel; +} + +/** + * @brief Adjust the number of slots in a ciphertext after downsampling. + * + * This function modifies the number of slots in the given ciphertext to improve + * performance by reducing the size of the polynomial being processed. + * + * @param encryptedInput Ciphertext whose number of slots will be adjusted. + * @param num_slots Desired number of slots in the ciphertext. + * + * @return Ciphertext with the updated number of slots. + */ + +Ctext FHEONHEController::change_num_slots(Ctext& encryptedInput, uint32_t num_slots){ + encryptedInput->SetSlots(1 << num_slots); + return encryptedInput; +} + +/** + * @brief Encode kernel data optimized for 3x3 kernels with padding of 1. + * + * This function takes 3D kernel data and encodes it into plaintexts, optimized + * for kernels of size 3x3 and padding of 1, using the specified encoding level. + * + * @param kernelData 3D vector containing kernel values to be encoded. + * @param cols_square Size of the column square for encoding. + * @param encode_level Encoding level to use for the plaintexts. + * + * @return Vector of plaintexts representing the encoded and optimized kernel data. + */ +vector FHEONHEController::encode_kernel_optimized(vector>>& kernelData, int cols_square, int encode_level) { + size_t dim1 = kernelData.size(); + if (dim1 == 0) return {}; + size_t dim2 = kernelData[0].size(); + if (dim2 == 0) return {}; + size_t dim3 = kernelData[0][0].size(); + if (dim3 == 0) return {}; + + int kernelWidth_sq = pow(dim2, 2); + vector> main_kernel(kernelWidth_sq, vector()); + for (size_t k=0; k< dim1; k++){ + vector> filters; + for (size_t i=0; i< dim2; i++){ + for (size_t j=0; j< dim3 ; j++){ + double cell_value = kernelData[k][i][j]; + // if(cell_value == 0) + // cell_value = 1e-40; + vector repeated(cols_square, cell_value); + filters.push_back(repeated); + } + } + for(int t =0; t< kernelWidth_sq; t++){ + main_kernel[t].insert(main_kernel[t].end(), filters[t].begin(), filters[t].end()); + } + } + + int vector_width = sqrt(cols_square); + vector> bin_masks = { + build_tiled_mask(vector_width + 1, 0, vector_width - 1, cols_square, dim1), + build_tiled_mask(vector_width, 0, cols_square, cols_square, dim1), + build_tiled_mask(vector_width, 0, vector_width - 1, cols_square, dim1), + build_tiled_mask(1, 0, vector_width - 1, cols_square, dim1), + build_tiled_mask(0, 0, cols_square, cols_square, dim1), + build_tiled_mask(0, 1, vector_width - 1, cols_square, dim1), + build_tiled_mask(1, vector_width - 1, vector_width - 1, cols_square, dim1), + build_tiled_mask(0, vector_width, cols_square, cols_square, dim1), + build_tiled_mask(0, vector_width + 1, vector_width - 1, cols_square, dim1) + }; + + vector encoded_kernel; + for (int s = 0; s < kernelWidth_sq; ++s) { + if (s >= static_cast(bin_masks.size())) { + std::cerr << "Error: bin_mask index out of range!" << std::endl; + return encoded_kernel; + } + + // Multiply main_kernel[s] element-wise with bin_masks[s] + + vector cleaned_kernel(main_kernel[s].size()); + for (size_t i = 0; i < main_kernel[s].size(); ++i) { + cleaned_kernel[i] = main_kernel[s][i] * bin_masks[s][i]; + } + + // Encode the cleaned kernel + int numElements = nextPowerOf2(main_kernel[s].size()); + Ptext encoded_val = encode_input(cleaned_kernel, numElements, encode_level); + encoded_kernel.push_back(encoded_val); + } + + return encoded_kernel; +} + +/** + * @brief Read the predicted label from encrypted inference data. + * + * This function decrypts and reads the predicted label from the given encrypted + * inference data. The result can be written to an output file. + * + * @param inferencedData Ciphertext containing the inference results. + * @param num_slots Number of elements in the ciphertext. + * @param outFile Output file stream to write the predicted label. + * + * @return The predicted label as an integer. + */ +int FHEONHEController::read_inferenced_label(Ctext inferencedData, int num_slots, ofstream& outFile){ + auto decryptedValue = decrypt_data(inferencedData, num_slots); + auto decryptedVector = decryptedValue->GetRealPackedValue(); + auto maxElementIt = max_element(decryptedVector.begin(), decryptedVector.end()); + int maxIndex = distance(decryptedVector.begin(), maxElementIt); + cout << "Predicted Value : " << maxIndex << " Weight: " << decryptedVector[maxIndex] << endl; + + if (outFile.is_open()) { + outFile << maxIndex << endl; + } else { + cout << "Unable to open file." << endl; + } + return 0; +} + +/** + * @brief Determine the minimum and maximum values from encrypted data. + * + * This helper function decrypts the inference data and computes the minimum + * and maximum values across all elements. + * + * @param inferencedData Ciphertext containing the inference data. + * @param num_slots Number of elements in the ciphertext. + * + * @return An integer representing the computed min or max value, depending on implementation. + */ +int FHEONHEController::read_minmax(Ctext inferencedData, int num_slots) { + auto decryptedValue = decrypt_data(inferencedData, num_slots); + auto decryptedVector = decryptedValue->GetRealPackedValue(); + + // cout << "Decrypted Vector " << decryptedVector << endl; + auto maxElementIt = max_element(decryptedVector.begin(), decryptedVector.end()); + int maxIndex = distance(decryptedVector.begin(), maxElementIt); + auto minElementIt = min_element(decryptedVector.begin(), decryptedVector.end()); + int minIndex = distance(decryptedVector.begin(), minElementIt); + cout << "------------------------------------------------------------------ " << endl; + cout << "Range [ " << decryptedVector[minIndex] << " , " << decryptedVector[maxIndex] <<" ]" << endl; + cout << "Index: " << maxIndex << endl; + cout << "------------------------------------------------------------------ " << endl; + return 0; +} + +/** + * @brief Retrieve the maximum value from encrypted convolution data for ReLU scaling. + * + * This temporary function decrypts the inference data and returns the maximum + * value, which can be used for scaling in the ReLU operation. + * + * @param inferencedData Ciphertext containing the inference data. + * @param num_slots Number of elements in the ciphertext. + * + * @return Maximum value as an integer. + */ +int FHEONHEController::read_scaling_value(Ctext inferencedData, int num_slots){ + auto decryptedValue = decrypt_data(inferencedData, num_slots); + auto decryptedVector = decryptedValue->GetRealPackedValue(); + + double maxAbsValue = *std::max_element(decryptedVector.begin(), decryptedVector.end(), [](int a, int b) { + return std::abs(a) < std::abs(b); + }); + int roundedMaxAbsValue = static_cast(std::ceil(std::abs(maxAbsValue))); + return roundedMaxAbsValue; +} + +/** + * @brief Build a tiled mask for optimized convolution operations. + * + * This function generates a mask of 0s and 1s to be element-wise multiplied + * with repeated kernel values in optimized convolution operations. + * + * @param starting_padding Number of zeros to pad at the start of the mask. + * @param ending_padding Number of zeros to pad at the end of the mask. + * @param window_length Length of the convolution window. + * @param max_length Maximum length of the mask. + * @param tile_count Number of times the pattern should be repeated. + * + * @return Vector of doubles representing the tiled mask. + */ +vector FHEONHEController::build_tiled_mask(int starting_padding, int ending_padding, + int window_length, int max_length, int tile_count) { + + vector mask; + + // Add starting padding + for (int i = 0; i < starting_padding; ++i) { + mask.push_back(0.0); + } + + // Add windows of 1s and a trailing 0 + while (mask.size() < static_cast(max_length - ending_padding)) { + for (int j = 0; j < window_length; ++j) { + mask.push_back(1.0); + } + mask.push_back(0.0); + } + + // Trim or pad the mask to match max_length + while (mask.size() > static_cast(max_length)) { + mask.pop_back(); + } + while (mask.size() < static_cast(max_length)) { + mask.push_back(0.0); + } + + // Add ending padding + for (int i = 0; i < ending_padding; ++i) { + mask[max_length - i - 1] = 0.0; + } + + // Tile the mask + std::vector tiled_mask; + for (int i = 0; i < tile_count; ++i) { + tiled_mask.insert(tiled_mask.end(), mask.begin(), mask.end()); + } + return tiled_mask; +} + + +/** + * @brief Read the predicted label from encrypted inference data. + * + * This function decrypts and reads the predicted label from the given encrypted + * inference data. The result can be written to an output file. + * + * @param inferencedData Ciphertext containing the inference results. + * @param num_slots Number of elements in the ciphertext. + * @param outFile Output file stream to write the predicted label. + * + * @return The predicted label as an integer. + */ +vector FHEONHEController::read_batch_inferenced_label(Ctext inferencedData, int batchSize, int numClasses, ofstream& outFile, int baseIndex){ + int totalElements = batchSize * numClasses; + + // Decrypt + auto decryptedVal = decrypt_data(inferencedData, totalElements); + auto decryptedVec = decryptedVal->GetRealPackedValue(); + cout << "DecryptedVec: " << decryptedVec << endl; + + vector predictedLabels(batchSize); + + // Process each batch separately + for (int b = 0; b < batchSize; b++) { + int startIdx = b * numClasses; + int endIdx = startIdx + numClasses; + + // Find max in this batch slice + + auto maxIt = max_element( + decryptedVec.begin() + startIdx, + decryptedVec.begin() + endIdx + ); + int label = distance(decryptedVec.begin() + startIdx, maxIt); + + // cout << "startIdx: " << startIdx + // << " endIdx: " << endIdx + // << " Elements: [ "; + // for (int i = startIdx; i < endIdx; i++) { + // cout << decryptedVec[i] << " "; + // } + // cout << "]\n"; + + predictedLabels[b] = label; + + cout << "Batch " << baseIndex+b + << " -> Predicted Label: " << label + << " (Score: " << *maxIt << ")" << endl; + + if (outFile.is_open()) { + outFile << label << endl; + } + } + + return predictedLabels; +} + + +vector FHEONHEController::read_batch_inferenced_label_multiple_outputs(vector inferencedData, int batchSize, int numClasses, ofstream& outFile, int baseIndex){ + + vector predictedLabels(batchSize); + // Process each batch separately + for (int b = 0; b < batchSize; b++) { + auto decryptedVal = decrypt_data(inferencedData[b], numClasses); + auto decryptedVec = decryptedVal->GetRealPackedValue(); + int startIdx = 0; + int endIdx = startIdx + numClasses; + + // Find max in this batch slice + auto maxIt = max_element( + decryptedVec.begin() + startIdx, + decryptedVec.begin() + endIdx + ); + int label = distance(decryptedVec.begin() + startIdx, maxIt); + predictedLabels[b] = label; + + cout << "Batch " << baseIndex+b + << " -> Predicted Label: " << label + << " (Score: " << *maxIt << ")" << endl; + + if (outFile.is_open()) { + outFile << label << endl; + } + } + return predictedLabels; +} + + +vector FHEONHEController::read_batch_scaling_values(vector& encryptedInputs, int inputChannels, int num_slots){ + vector maxVec; + for(int i=0; iGetRealPackedValue(); + double maxAbsVal = *std::max_element(decryptedVec.begin(), decryptedVec.end(), [](double a, double b) { + return std::abs(a) < std::abs(b); + }); + int roundedMax = static_cast(std::ceil(std::abs(maxAbsVal))); + maxVec.push_back(roundedMax); + } + return maxVec; +} + +int FHEONHEController::read_batch_minmax(vector& encryptedInputs, int inputChannels, int num_slots) { + + vector maxVec; + for(int i=0; iGetRealPackedValue(); + + auto maxElementIt = max_element(decryptedVec.begin(), decryptedVec.end()); + int maxIndex = distance(decryptedVec.begin(), maxElementIt); + auto minElementIt = min_element(decryptedVec.begin(), decryptedVec.end()); + int minIndex = distance(decryptedVec.begin(), minElementIt); + cout << "------------------------------------------------------------------ " << endl; + cout << "Range [ " << decryptedVec[minIndex] << " , " << decryptedVec[maxIndex] <<" ]" << endl; + cout << "Index: " << maxIndex << endl; + cout << "------------------------------------------------------------------ " << endl; + } + return 0; +} + +/** + * @brief Decrypts and prints a batch of encrypted inputs. + * + * This function iterates through a vector of ciphertexts, decrypts each + * encrypted input using the internally stored secret key, and prints + * the real packed values to stdout. + * + * @param encryptedInputs Vector of encrypted ciphertext inputs. + * @param inputChannels Number of encrypted inputs (channels) to decrypt. + * @param num_slots Number of CKKS slots to decode. + * + * @return Returns 0 on successful execution. + */ +int FHEONHEController::decrypt_batch_data(vector& encryptedInputs, int inputChannels, int num_slots) { + + vector maxVec; + for(int i=0; iGetRealPackedValue(); + cout << "------------------------------------------------------------------ " << endl; + cout << decryptedVec << endl; + cout << "------------------------------------------------------------------ " << endl; + } + return 0; +} + + +/** + * @brief Decrypts a packed CKKS ciphertext and prints the result. + * + * This function decrypts the provided ciphertext using the controller's + * secret key, sets the plaintext length to the specified number of slots, + * extracts the complex CKKS packed values, and prints them. + * + * @param encryptedpackedVector Encrypted CKKS ciphertext. + * @param num_slots Number of slots to extract from the decrypted plaintext. + */ +void FHEONHEController::decrypt_and_print(Ctext encryptedpackedVector, int num_slots) { + + Ptext plaintextDec; + context->Decrypt(keyPair.secretKey, encryptedpackedVector, &plaintextDec); + plaintextDec->SetLength(num_slots); + vector> finalResult = plaintextDec->GetCKKSPackedValue(); + cout << finalResult << endl; + cout << endl; +} + +/** + * @brief Decrypts ciphertext using a provided private key. + * + * This function decrypts the given ciphertext using the specified secret key, + * sets the plaintext length, and returns the decrypted plaintext object. + * + * @param sk Private key used for decryption. + * @param encryptedinputData Ciphertext to decrypt. + * @param cols Number of plaintext slots to retain. + * + * @return Decrypted plaintext (Ptext). + */ +Ptext FHEONHEController::decrypt_data_with_key(PrivateKey &sk, + Ctext encryptedinputData, + int cols) { + + Ptext plaintextDec; + context->Decrypt(sk, encryptedinputData, &plaintextDec); + plaintextDec->SetLength(cols); + return plaintextDec; +} + +/** + * @brief Reads and computes the scaling value from decrypted inference data. + * + * This function decrypts inference output, retrieves the real packed values, + * and computes the maximum absolute value across all slots. The result is + * rounded up to the nearest integer and returned as the scaling factor. + * + * @param sk Private key used for decryption. + * @param inferencedData Encrypted inference output. + * @param num_slots Number of slots to decode. + * + * @return Integer representing the ceiling of the maximum absolute value. + */ +int FHEONHEController::read_scaling_value_with_key(PrivateKey &sk, + Ctext inferencedData, + int num_slots) { + // int roundedMaxAbsValue = 10; // Temporary hardcoded value for testing + auto decryptedValue = decrypt_data_with_key(sk, inferencedData, num_slots); + auto decryptedVector = decryptedValue->GetRealPackedValue(); + + // cout << endl + // << "--------------------------------------------------- " << endl + // << endl; + // cout << "Decrypted Vector for Scaling Value: " << decryptedVector << endl; + // cout << endl + // << "--------------------------------------------------- " << endl; + + double maxAbsValue = + *std::max_element(decryptedVector.begin(), decryptedVector.end(), + [](int a, int b) { return std::abs(a) < std::abs(b); }); + int roundedMaxAbsValue = static_cast(std::ceil(std::abs(maxAbsValue))); + return roundedMaxAbsValue; +} + + +/** + * @brief Decrypts inference output and determines the predicted label. + * + * This function decrypts the inference ciphertext, extracts the real packed + * values, determines the index of the maximum value (argmax), and prints the + * predicted label and its corresponding weight. If the output file stream is + * open, the predicted label is written to the file. + * + * @param sk Private key used for decryption. + * @param inferencedData Encrypted inference result. + * @param num_slots Number of slots to decode. + * @param outFile Output file stream to write predicted label. + * + * @return Index corresponding to the predicted label. + */ +int FHEONHEController::read_inferenced_label_with_key(PrivateKey &sk, + Ctext inferencedData, + int num_slots, + ofstream &outFile) { + auto decryptedValue = decrypt_data_with_key(sk, inferencedData, num_slots); + auto decryptedVector = decryptedValue->GetRealPackedValue(); + auto maxElementIt = + max_element(decryptedVector.begin(), decryptedVector.end()); + int maxIndex = distance(decryptedVector.begin(), maxElementIt); + cout << "Predicted Value : " << maxIndex + << " Weight: " << decryptedVector[maxIndex] << endl; + cout << "Decrypted Vector: " << decryptedVector << endl; + + if (outFile.is_open()) { + outFile << maxIndex << endl; + } else { + cout << "Unable to open file." << endl; + } + return maxIndex; +} + +void FHEONHEController::harness_generate_rotation_keys( CryptoContext &crypto_context, PrivateKey &sk, + vector rotations_positions, ofstream &key_file, bool sum_key) { + + if(sum_key){ + crypto_context->EvalSumKeyGen(sk); + } + crypto_context->EvalRotateKeyGen(sk, rotations_positions); + + if (key_file.is_open()) { + if (!crypto_context->SerializeEvalAutomorphismKey(key_file, SerType::BINARY)) { + cerr << "Error writing rotation keys" << std::endl; + exit(1); + } + } else { + cerr << "Error serializing Rotation keys" << endl; + exit(1); + } +} + +void FHEONHEController::harness_generate_bootstrapping_and_rotation_keys( CryptoContext &crypto_context, PrivateKey &sk, + vector rotations_positions, ofstream &key_file, bool sum_key) { + + uint32_t numSlots = crypto_context->GetRingDimension() / 2; + std::vector levelBudget = {3, 3}; + crypto_context->EvalBootstrapSetup(levelBudget); + crypto_context->EvalBootstrapKeyGen(sk, numSlots); + crypto_context->EvalRotateKeyGen(sk, rotations_positions); + + if(sum_key){ + crypto_context->EvalSumKeyGen(sk); + } + + if (key_file.is_open()) { + if (!crypto_context->SerializeEvalAutomorphismKey(key_file, SerType::BINARY)) { + cerr << "Error writing rotation keys" << std::endl; + exit(1); + } + } else { + cerr << "Error serializing Rotation keys" << endl; + exit(1); + } +} + +void FHEONHEController::harness_clear_bootstrapping_and_rotation_keys(CryptoContext &crypto_context) { + // crypto_context->ClearEvalMultKeys(); + crypto_context->ClearEvalAutomorphismKeys(); + // CryptoContextFactory::ReleaseAllContexts(); +} + +void FHEONHEController::read_evaluation_keys(CryptoContext &crypto_context, const string &pubkey_dir, + const string &mult_file, const string &rot_file) { + + crypto_context->ClearEvalMultKeys(); + crypto_context->ClearEvalAutomorphismKeys(); + // DO NOT call ReleaseAllContexts() as it breaks existing ciphertexts + + // Open files in READ mode + ifstream multkey_file(pubkey_dir + mult_file, ios::in | ios::binary); + ifstream rotkey_file(pubkey_dir + rot_file, ios::in | ios::binary); + + // Deserialize Multiplication key into EXISTING context + if (!multkey_file.is_open() || + !crypto_context->DeserializeEvalMultKey(multkey_file, SerType::BINARY)) { + throw std::runtime_error("Failed to load relinearization key from " + + pubkey_dir + mult_file); + } + + // Deserialize Automorphism keys into EXISTING context + if (!rotkey_file.is_open() || !crypto_context->DeserializeEvalAutomorphismKey( + rotkey_file, SerType::BINARY)) { + throw std::runtime_error("Failed to load rotation keys from " + pubkey_dir + + rot_file); + } + // cout << "Successfully loaded evaluation keys" << endl; +} + +void FHEONHEController::read_evaluation_keys(CryptoContext &crypto_context, const string &pubkey_dir, + const string &mult_file, const string &rot_file, const string &sk_path) { + + // Clear existing evaluation keys from the context + crypto_context->ClearEvalMultKeys(); + crypto_context->ClearEvalAutomorphismKeys(); + // DO NOT call ReleaseAllContexts() as it breaks existing ciphertexts + + // Open files in READ mode + ifstream multkey_file(pubkey_dir + mult_file, ios::in | ios::binary); + ifstream rotkey_file(pubkey_dir + rot_file, ios::in | ios::binary); + + // Deserialize Multiplication key into EXISTING context + if (!multkey_file.is_open() || + !crypto_context->DeserializeEvalMultKey(multkey_file, SerType::BINARY)) { + throw std::runtime_error("Failed to load relinearization key from " + + pubkey_dir + mult_file); + } + + // Deserialize Automorphism keys into EXISTING context + if (!rotkey_file.is_open() || !crypto_context->DeserializeEvalAutomorphismKey( + rotkey_file, SerType::BINARY)) { + throw std::runtime_error("Failed to load rotation keys from " + pubkey_dir + + rot_file); + } +} \ No newline at end of file diff --git a/submissions/cifar10/include/encryption_utils.h b/submissions/cifar10/include/encryption_utils.h new file mode 100644 index 0000000..08ce866 --- /dev/null +++ b/submissions/cifar10/include/encryption_utils.h @@ -0,0 +1,50 @@ +// Copyright 2025 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#ifndef input_encryptION_UTILS_H_ +#define input_encryptION_UTILS_H_ + +#include "openfhe.h" +#include "params.h" + +using namespace lbcrypto; +using CiphertextT = ConstCiphertext; +using CCParamsT = CCParams; +using CryptoContextT = CryptoContext; +using EvalKeyT = EvalKey; +using PlaintextT = Plaintext; +using PrivateKeyT = PrivateKey; +using PublicKeyT = PublicKey; + +#define MNIST_DIM 784 +#define CIFAR_DIM 3072 +#define NORMALIZED_DIM 4096 + +struct Sample { + float image[NORMALIZED_DIM]; +}; + +ConstCiphertext input_encrypt(CryptoContext cc, + std::vector input, + PublicKey pk); +std::vector input_decrypt(CryptoContextT v11343, CiphertextT v11344, + PrivateKeyT v11345); +PublicKey read_public_key(const InstanceParams &prms); +PrivateKey read_secret_key(const InstanceParams &prms); +CryptoContext read_crypto_context(const InstanceParams &prms); +void read_eval_keys(const InstanceParams &prms, CryptoContextT cc); +void load_dataset(std::vector &dataset, const char *filename, int dim, + int max_samples = -1); +int argmax(float *A, int N); + +#endif // ifndef input_encryptION_UTILS_H_ \ No newline at end of file diff --git a/submissions/cifar10/include/fheon/FHEONANNController.h b/submissions/cifar10/include/fheon/FHEONANNController.h new file mode 100755 index 0000000..b187757 --- /dev/null +++ b/submissions/cifar10/include/fheon/FHEONANNController.h @@ -0,0 +1,174 @@ + +/*********************************************************************************************************************** +* +* @author: Nges Brian, Njungle +* +* MIT License +* Copyright (c) 2025 Secure, Trusted and Assured Microelectronics, Arizona State +University + +* Permission is hereby granted, free of charge, to any person obtaining a copy +* of this software and associated documentation files (the "Software"), to deal +* in the Software without restriction, including without limitation the rights +* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +* copies of the Software, and to permit persons to whom the Software is +* furnished to do so, subject to the following conditions: + +* The above copyright notice and this permission notice shall be included in all +* copies or substantial portions of the Software. + +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +********************************************************************************************************************/ + +/******************************************************************************************************************* + * This ANN controller is used to define all ANN layers used in this project + * such as; Convolution, avgpool, fclinear + *******************************************************************************************************************/ + +#ifndef FHEON_ANNCONCROLLER_H +#define FHEON_ANNCONCROLLER_H + +#include +#include + +#include "FHEONHEController.h" + +#include "Utils.h" +#include "UtilsData.h" + +using namespace lbcrypto; +using namespace std; + +/** he_FHEONANNController defined utils */ +using namespace utils; +using namespace utilsdata; + +class FHEONANNController { + +private: + CryptoContext context; + +public: + string public_data = "sskeys"; + int num_slots = 1 << 14; + + FHEONANNController(CryptoContext &ctx) : context(ctx) {} + void setContext(CryptoContext &in_context); + void setNumSlots(int numSlots) { num_slots = 1 << numSlots; } + + vector generate_convolution_rotation_positions(int inputWidth, + int inputChannels, + int outputChannels, + int kernelWidth, + int padding, int Stride); + vector generate_linear_rotation_positions(int maxFCLayeroutputs, + int rotationPosition); + vector generate_avgpool_rotation_positions(int inputWidth, + int kernelWidth, int Stride, + int inputChannels); + + vector generate_optimized_convolution_rotation_positions( + int inputWidth, int inputChannels, int outputChannels, int Stride = 1, + string stridingType = "multi_channels"); + vector generate_avgpool_optimized_rotation_positions( + int inputWidth, int inputChannels, int kernelWidth, int Stride, + bool globalPooling = false, string stridingType = "multi_channels", + int rotationIndex = 16); + + Ctext he_convolution(Ctext &encryptedInput, vector> &kernelData, + Ptext &biasInput, int inputWidth, int inputChannels, + int outputChannels, int kernelWidth, int padding = 0, + int stride = 1); + Ctext he_convolution_advanced(Ctext &encryptedInput, + vector> &kernelData, + Ptext &biasInput, int inputWidth, + int inputChannels, int outputChannels, + int kernelWidth, int padding, int stride); + Ctext he_convolution_optimized(Ctext &encryptedInput, + vector> &kernelData, + Ptext &biasInput, int inputWidth, + int inputChannels, int outputChannels, + int Stride = 1, int index = 0); + Ctext he_convolution_optimized_with_multiple_channels( + Ctext &encryptedInput, vector> &kernelData, + Ptext &biasInput, int inputWidth, int inputChannels, int outputChannels); + Ctext he_shortcut_convolution(Ctext &encryptedInput, + vector &kernelData, Ptext &biasInput, + int inputWith, int inputChannels, + int outputChannels); + vector he_convolution_and_shortcut_optimized( + const Ctext &encryptedInput, const vector> &kernelData, + const vector &shortcutKernelData, Ptext &biasVector, + Ptext &shortcutBiasVector, int inputWidth, int inputChannels, + int outputChannels); + vector he_convolution_and_shortcut_optimized_with_multiple_channels( + const Ctext &encryptedInput, const vector> &kernelData, + const vector &shortcutKernelData, Ptext &biasInput, + Ptext &shortcutBiasInput, int inputWidth, int inputChannels, + int outputChannels); + + Ctext he_avgpool(Ctext encryptedInput, int imgCols, int outputChannels, + int kernelWidth = 2, int Stride = 2); + Ctext he_avgpool_advanced(Ctext encryptedInput, int inputWidth, + int outputChannels, int kernelWidth, int stride, + int padding); + Ctext he_avgpool_optimzed(Ctext &encryptedInput, int inputWidth, + int outputChannels, int kernelWidth, int Stride); + Ctext he_avgpool_optimzed_with_multiple_channels(Ctext &encryptedInput, + int inputWidth, + int inputChannels, + int kernelWidth, int Stride); + Ctext he_globalavgpool(Ctext &encryptedInput, int inputWidth, + int outputChannels, int kernelWidth, + int rotatePositions); + + Ctext he_linear(Ctext &encryptedInput, vector &weightMatrix, + Ptext &biasInput, int inputSize, int outputSize, + int rotatePositions); + Ctext he_linear_optimized(Ctext &encryptedInput, vector &weightMatrix, + Ptext &biasInput, int inputSize, int outputSize); + + Ctext he_relu(Ctext &encryptedInput, double scale, int vectorSize, + int polyDegree = 59); + Ctext he_sum_two_ciphertexts(Ctext &firstInput, Ctext &secondInput); + +private: + Ctext basic_striding(Ctext in_cipher, int inputWidth, int widthOut, + int Stride); + Ctext downsample(const Ctext &input, int inputWidth, int stride); + Ctext downsample_with_multiple_channels(const Ctext &input, int inputWidth, + int stride, int numChannels); + Ctext batch_convolution_operation(const vector &rotatedInputs, + const vector &kernelData, + int kernelWidth, int inputSize, + int inputChannels); + + Ptext first_mask(int width, int inputSize, int stride, int level); + Ptext first_mask_with_channels(int width, int inputSize, int stride, + int numChannels, int level); + + Ptext generate_binary_mask(int pattern, int inputSize, int stride, int level); + Ptext generate_binary_mask_with_channels(int pattern, int inputSize, + int stride, int numChannels, + int level); + + Ptext generate_row_mask(int row, int width, int inputSize, int stride, + int level); + Ptext generate_row_mask_with_channels(int row, int width, int inputSize, + int stride, int numChannels, int level); + + Ptext generate_zero_mask(int size, int level); + Ptext generate_zero_mask_channels(int size, int numChannels, int level); + Ptext generate_channel_full_mask(int n, int in_elements, int out_elements, + int numChannels, int level); + Ptext generate_channel_mask_with_zeros(int channel, int outputSize, + int numChannels, int level); +}; + +#endif // FHEON_ANNCONCROLLER_H \ No newline at end of file diff --git a/submissions/cifar10/include/fheon/FHEONHEController.h b/submissions/cifar10/include/fheon/FHEONHEController.h new file mode 100755 index 0000000..e17854f --- /dev/null +++ b/submissions/cifar10/include/fheon/FHEONHEController.h @@ -0,0 +1,189 @@ + +/*********************************************************************************************************************** +* +* @author: Nges Brian, Njungle +* +* MIT License +* Copyright (c) 2025 Secure, Trusted and Assured Microelectronics, Arizona State +University + +* Permission is hereby granted, free of charge, to any person obtaining a copy +* of this software and associated documentation files (the "Software"), to deal +* in the Software without restriction, including without limitation the rights +* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +* copies of the Software, and to permit persons to whom the Software is +* furnished to do so, subject to the following conditions: + +* The above copyright notice and this permission notice shall be included in all +* copies or substantial portions of the Software. + +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +********************************************************************************************************************/ + +/******************************************************************** + * This FHE controller is used to define basic FHE functions used across the + * project such as: Context generation, bootstrapping, encryption + ********************************************************************/ + +#ifndef FHEON_FHEONHEController_H +#define FHEON_FHEONHEController_H + +#include +#include + +#include +#include +#include +#include +// #include "schemeswitching-data-serializer.h" + +#include "Utils.h" +#include "UtilsData.h" +#include "UtilsImage.h" + +using namespace lbcrypto; +using namespace std; +using namespace std::chrono; + +using namespace utils; +using namespace utilsdata; +using namespace utilsimages; + +using Ptext = Plaintext; +using Ctext = Ciphertext; + +class FHEONHEController { + +protected: + CryptoContext context; + +public: + int circuit_depth; + int num_slots; + int pLWE; + int mult_depth = 10; + string keys_folder = "./../../io/single/"; + string cc_prefix = "./secret_key/cc.bin"; + string pk_prefix = "./secret_key/sk.bin"; + string rotation_prefix = "./public_keys/rk.bin"; + string mult_prefix = "./public_keys/mt.bin"; + string sum_prefix = "./public_keys/sm.bin"; + string sk_prefix = "./secret_key/sk.bin"; + + FHEONHEController(CryptoContext ctx) : context(ctx) {} + + CryptoContext getContext() const { return context; } + void setContext(CryptoContext ctx) { context = ctx; } + + double getlogPQ(const DCRTPoly &poly); + + /* + * Generating context, bootstrapping keys, rotation keys and loading them */ + void generate_context(int ringDim = 14, int numSlots = 12, + int mlevelBootstrap = 10, bool serialize = true); + void generate_context(int ringDim = 15, int numSlots = 14, + int mlevelBootstrap = 10, int dcrtBits = 55, + int firstMod = 56, int numDigits = 3, + vector levelBudget = {4, 4}, + bool serialize = true); + + void generate_bootstrapping_keys(int bootstrap_slots, string filename, + bool serialize); + void generate_rotation_keys(vector rotations, string filename = "", + bool serialize = true); + void generate_bootstrapping_and_rotation_keys(vector rotations, + int bootstrap_slots, + const string &filename, + bool serialize); + + void load_context(bool verbose = false); + void load_rotation_keys(const string &filename, bool verbose = false); + void load_bootstrapping_and_rotation_keys(int bootstrap_slots, + const string &filename, + bool verbose = false); + + void clear_rotation_keys(); + void clear_context(int bootstrapping_key_slots); + void clear_bootstrapping_and_rotation_keys(int bootstrap_num_slots); + Ctext bootstrap_function(Ctext &encryptedInput, int level = 2); + vector batch_bootstrap_function(vector& encryptedInputs, int inputChannels, int encode_level = 2); + + /*** Encrypt and decrypt packed ciphertext. used to encrypt image and decrpt + * the results ****/ + Ctext encrypt_input(vector &inputData); + Ctext reencrypt_data(Ptext plaintextInput); + Ptext encode_input(vector &inputData, int encode_level = 1); + Ptext encode_input(vector &inputData, int num_slots, + int encode_level = 1); + Ptext decrypt_data(Ctext encryptedInput, int cols); + + vector> + encrypt_kernel(vector>> &kernelData, int colsSquare); + vector encode_kernel(vector>> &kernelData, + int colsSquare); + vector encode_kernel(vector &kernelData, int colsSquare); + vector + encode_kernel_optimized(vector>> &kernelData, + int colsSquare, int encode_levels = 1); + Ptext encode_shortcut_kernel(vector &inputData, int colsSquare); + Ptext encode_bais_input(vector &inputData, int colsSquare, + int encode_levels = 1); + + Ctext change_num_slots(Ctext &encryptedInput, uint32_t numSlots); + + int read_inferenced_label(Ctext encryptedInput, int noElements, + ofstream &outFile); + int read_minmax(Ctext encryptedInput, int noElements); + int read_scaling_value(Ctext encryptedInput, int noElements); + + vector read_batch_inferenced_label(Ctext inferencedData, int batchSize, + int numClasses, ofstream &outFile, + int baseIndex); + vector read_batch_inferenced_label_multiple_outputs(vector inferencedData, + int batchSize, int numClasses, + ofstream &outFile, int baseIndex); + vector read_batch_scaling_values(vector &encryptedInputs, + int inputChannels, int num_slots); + int read_batch_minmax(vector &encryptedInputs, int inputChannels, + int num_slots); + int decrypt_batch_data(vector &encryptedInputs, int inputChannels, + int num_slots); + void decrypt_and_print(Ctext encryptedpackedVector, int num_slots); + + Ptext decrypt_data_with_key(PrivateKey &sk, + Ctext encryptedinputData, int cols); + int read_scaling_value_with_key(PrivateKey &sk, + Ctext encryptedInput, int noElements); + int read_inferenced_label_with_key(PrivateKey &sk, + Ctext encryptedInput, int noElements, + ofstream &outFile); + + void harness_generate_bootstrapping_and_rotation_keys(CryptoContext &crypto_context, PrivateKey &sk, + vector rotations_positions, ofstream &key_file, bool sum_keys = false); + void harness_clear_bootstrapping_and_rotation_keys(CryptoContext &crypto_context); + + void read_evaluation_keys(CryptoContext &crypto_context, const string &pubkey_dir, + const string &mult_file, const string &rot_file); + void read_evaluation_keys(CryptoContext &crypto_context, const string &pubkey_dir, + const string &mult_file, const string &rot_file, const string &sk_path); + + void harness_generate_rotation_keys(CryptoContext &crypto_context, PrivateKey &sk, + vector rotations_positions, ofstream &key_file, bool sum_key); + +private: + KeyPair keyPair; + vector level_budget = {4, 4}; + vector bsgsDim = {0, 0}; + vector build_tiled_mask(int starting_padding, int ending_padding, + int window_length, int max_length, int tile_count); + + void keys_serialization(); +}; + +#endif // FHEON_FHEONHEController_H diff --git a/submissions/cifar10/include/fheon/Utils.h b/submissions/cifar10/include/fheon/Utils.h new file mode 100755 index 0000000..71f9e81 --- /dev/null +++ b/submissions/cifar10/include/fheon/Utils.h @@ -0,0 +1,152 @@ + +/*********************************************************************************************************************** +* +* @author: Nges Brian, Njungle +* +* MIT License +* Copyright (c) 2025 Secure, Trusted and Assured Microelectronics, Arizona State University + +* Permission is hereby granted, free of charge, to any person obtaining a copy +* of this software and associated documentation files (the "Software"), to deal +* in the Software without restriction, including without limitation the rights +* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +* copies of the Software, and to permit persons to whom the Software is +* furnished to do so, subject to the following conditions: + +* The above copyright notice and this permission notice shall be included in all +* copies or substantial portions of the Software. + +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +********************************************************************************************************************/ + +/** + * @file utils + * @brief Plain secure utility functions used as general helpers throughout the project. + * + * This file defines various helper functions for secure computations and + * homomorphic operations, providing reusable utilities across the project. + */ + +#ifndef FHEON_UTILS_H +#define FHEON_UTILS_H + +#include +#include + +using namespace std; +using namespace std::chrono; +using namespace lbcrypto; +using Ctext = Ciphertext; + +namespace utils { + + static duration> total_time; + + /** + * @brief Print the welcome message with project and author details. + */ + static inline void printWelcomeMessage(){ + cout<< "----------------------------------------------------------------------------------- " << endl; + cout<< "-------------------------------- WELCOME TO FHEON --------------------------------- " << endl; + cout<< "------------------ Nges Brian, Eric Jahns, Michel A. Kinsy ------------------------ " << endl; + cout<< "---------- Secure, Trusted and Assured Microelectronics (STAM) CENTER ------------- " << endl; + cout<< "---------------------------- Arizona State University ----------------------------- " << endl; + cout << endl; + } + + /** + * @brief Get the current time point for timing measurements. + * + * @return Current time point. + */ + static inline chrono::time_point startTime() { + return steady_clock::now(); + } + + /** + * @brief Print duration since start time, optionally tracking global execution time. + * + * @param start Start time point. + * @param caption Caption for printed output (default: "Time Taken is:"). + * @param global_time If true, accumulates total runtime across calls. + */ + static inline void printDuration(chrono::time_point start, const string &caption="Time Taken is: ", bool global_time=false) { + auto ms = duration_cast(steady_clock::now() - start); + + static duration> total_duration; + if(global_time){ + total_duration = total_time + ms; + total_time = total_duration; + } + else{ + total_duration = ms; + } + + auto secs = duration_cast(ms); + ms -= duration_cast(secs); + auto mins = duration_cast(secs); + secs -= duration_cast(mins); + + cout<< endl; + if (mins.count() < 1) { + cout << "------- " << caption << ": " << secs.count() << ":" << ms.count() << "s" << " (Total: " << duration_cast(total_duration).count() << "s)" << " -------- " << endl; + } else { + cout << "-------- " << caption << ": " << mins.count() << "." << secs.count() << ":" << ms.count() << " (Total: " << duration_cast(total_duration).count() << "mins)" << " -------- " << endl; + } + cout<< endl; + } + + /** + * @brief Print bootstrapping metadata for a ciphertext. + * + * @param ciphertextIn Input ciphertext. + * @param depth Maximum depth available. + */ + static inline void printBootsrappingData(Ctext ciphertextIn, int depth){ + std::cout << "Number of levels remaining: " + << depth - ciphertextIn->GetLevel() - (ciphertextIn->GetNoiseScaleDeg() - 1) + << " ***Level: " << ciphertextIn->GetLevel() << " ***noiseScaleDeg: " + << ciphertextIn->GetNoiseScaleDeg() << std::endl; + } + + /** + * @brief Measure elapsed time between two time points in seconds. + * + * @param start Start time. + * @param end End time. + * @return Elapsed time in seconds. + */ + static inline int measureTime(const time_point& start, const time_point& end) { + auto duration = duration_cast(end - start); + return duration.count(); + } + + /** + * @brief Get current time point (high resolution). + * + * @return Current time point. + */ + static inline time_point get_current_time() { + return high_resolution_clock::now(); + } + + /** + * @brief Compute total time from a vector of durations. + * + * @param measuring Vector of time values (seconds). + * @return Total time in seconds. + */ + static inline int totalTime(vector measuring){ + int total = accumulate(measuring.begin(), measuring.end(), 0); + cout << "------- Circuit Total Time: " << total << endl; + return total; + } + +} + +#endif //FHEON_UTILS_H \ No newline at end of file diff --git a/submissions/cifar10/include/fheon/UtilsData.h b/submissions/cifar10/include/fheon/UtilsData.h new file mode 100755 index 0000000..c695372 --- /dev/null +++ b/submissions/cifar10/include/fheon/UtilsData.h @@ -0,0 +1,510 @@ + +/*********************************************************************************************************************** +* +* @author: Nges Brian, Njungle +* +* MIT License +* Copyright (c) 2025 Secure, Trusted and Assured Microelectronics, Arizona State University + +* Permission is hereby granted, free of charge, to any person obtaining a copy +* of this software and associated documentation files (the "Software"), to deal +* in the Software without restriction, including without limitation the rights +* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +* copies of the Software, and to permit persons to whom the Software is +* furnished to do so, subject to the following conditions: + +* The above copyright notice and this permission notice shall be included in all +* copies or substantial portions of the Software. + +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +********************************************************************************************************************/ + +/** + * @file DataUtils + * @brief Manage data generation, reading from files, and arranging for use in networks. + * + * This file provides functions to handle data preparation tasks, including + * generating random datasets, reading data from files for different datasets, and organizing it for use + * in HE-friendly neural networks. + */ + +#ifndef FHEON_DATAUTILS_H +#define FHEON_DATAUTILS_H + +#include +#include +#include + +using namespace std; +using namespace std::chrono; +using namespace lbcrypto; + +namespace utilsdata { + + /** + * @brief Print a 1D vector of doubles. + * + * @param vecData Vector to print. + */ + static inline void printVector(vector &vecData) { + cout << vecData <> matrix2D){ + int rows = matrix2D.size(); + int cols = matrix2D[0].size(); + for (int i = 0; i < rows; ++i) { + for (int j = 0; j < cols; ++j) { + cout << matrix2D[i][j] << " "; + } + cout << endl; + } + cout << endl; + } + + /** + * @brief Print a 3D matrix of doubles. + * + * @param matrix3D Matrix to print. + */ + static inline void print3DMatrix(vector>> matrix3D){ + int depth = matrix3D.size(); + int rows = matrix3D[0].size(); + int cols = matrix3D[0][0].size(); + for (int d = 0; d < depth; ++d) { + cout << "Depth " << d << ":\n"; + for (int i = 0; i < rows; ++i) { + for (int j = 0; j < cols; ++j) { + cout << matrix3D[d][i][j] << " "; + } + cout << endl; + } + cout << endl; + } + } + /** + * @brief Create a 1D vector with random values. + * + * @param cols Number of elements. + * @param minValue Minimum random value. + * @param maxValue Maximum random value. + * @return Random vector of doubles. + */ + static inline vector createVector(int cols, int minValue, int maxValue) { + // Initialize random seed + static bool seedInitialized = false; + if (!seedInitialized) { + srand(static_cast(time(nullptr))); + seedInitialized = true; + } + + vector vectorData(cols); + for (int i = 0; i < cols; i++) { + vectorData[i] = minValue + static_cast(rand()) / RAND_MAX * (maxValue - minValue); + } + return vectorData; + } + + /** + * @brief Create a 2D matrix with random values. + * + * @param rows Number of rows. + * @param cols Number of columns. + * @param minValue Minimum random value. + * @param maxValue Maximum random value. + * @return Random 2D matrix. + */ + static inline vector> create2DMatrix(int rows, int cols, int minValue, int maxValue) { + vector> matrix2D; + matrix2D.reserve(rows); // Reserve space to avoid multiple allocations + + for (int i = 0; i < rows; i++) { + matrix2D.push_back(createVector(cols, minValue, maxValue)); + } + return matrix2D; + } + + /** + * @brief Create a 3D matrix with random values. + * + * @param depth Depth of matrix. + * @param rows Number of rows per slice. + * @param cols Number of columns per slice. + * @param minValue Minimum random value. + * @param maxValue Maximum random value. + * @return Random 3D matrix. + */ + static inline vector>> create3DMatrix(int depth, int rows, int cols, int minValue, int maxValue) { + vector>> matrix3D; + matrix3D.reserve(depth); // Reserve space to avoid multiple allocations + + for (int d = 0; d < depth; d++) { + matrix3D.push_back(create2DMatrix(rows, cols, minValue, maxValue)); + } + return matrix3D; + } + + /** + * @brief Flatten a 3D matrix into a 1D vector. + * + * @param matrix3D Matrix to flatten. + * @return Flattened vector. + */ + static inline vector flatten3DMatrix(const vector>> matrix3D) { + vector flatVec; + for (const auto& matrix : matrix3D) { + for (const auto& row : matrix) { + for (const auto& elem : row) { + flatVec.push_back(elem); + } + } + } + return flatVec; + } + + /** + * @brief Print a plaintext vector after decryption. + * + * @param packedVec Plaintext vector to print. + */ + static inline void printPtextVector(Plaintext packedVec) { + vector> finalResult = packedVec->GetCKKSPackedValue(); + cout << finalResult << endl; + cout << endl; + } + + + /** + * @brief Generate a binary mask of ones followed by zeros. + * + * @param ones_width Number of ones. + * @param vector_size Total size of mask. + * @return Mask vector. + */ + static inline vector generate_mixed_mask(int ones_size, int vector_size){ + vector ones_vector(ones_size, 1.0); + vector zeros_vector((vector_size - ones_size), 0.0); + ones_vector.insert(ones_vector.end(), zeros_vector.begin(), zeros_vector.end()); + return ones_vector; + } + + /** + * @brief Generate a scaled mask with uniform values. + * + * @param scale_value Scaling factor. + * @param vector_size Total size of mask. + * @return Scaled mask vector. + */ + static inline vector generate_scale_mask(int scale_value, int vector_size){ + double scale_val = (1.0/scale_value); + + vector scaled_vector(vector_size, scale_val); + return scaled_vector; + } + + /** + * @brief Generate a value mask with a fixed value. + * + * @param scale_value Value to assign. + * @param vector_size Total size of mask. + * @return Value mask vector. + */ + static inline vector generate_value_mask(double scale_value, int vector_size){ + + vector scaled_vector(vector_size, scale_value); + return scaled_vector; + } + + /** + * @brief Approximate greater-than function for spiking. + * + * @param x Input value. + * @return Spike value if x > 0, else 0. + */ + static inline int greaterFunction(double x) { + double threshold_value = 0; + double spike_value = 0; + int scale_value = 10; + if(x > threshold_value){ + spike_value = x*scale_value; + return spike_value; + + } + else{ + return 0; + } + } + + /** + * @brief Approximate smooth greater-than step function. + * + * @param x Input value. + * @return Smoothed spike value. + */ + static inline double approximateGreaterFunction(double x){ + double threshold_value = 0.05; + double steepness = 100.0; + double spike_value = 0.5 * (1 + tanh(steepness * (x - threshold_value))); + return spike_value; + } + + /** + * @brief ReLU with scaling factor. + * + * @param x Input value. + * @param scale Scaling factor. + * @return ReLU output. + */ + static inline double innerRelu(double x, double scale){ + if (x < 0) return 0; else return (1 / scale) * x; + } + + + /** + * @brief Create an average pooling filter. + * + * @param kernel_width Width of pooling kernel. + * @return Averaging filter vector. + */ + static inline vector avgpoolFilter(int kernel_width){ + int numVals = pow(kernel_width, 2); + double scaled_value = (1.0/numVals); + vector avgpoolFilter(numVals, scaled_value); + + return avgpoolFilter; + } + + + /** + * @brief Return the next power of 2 greater than or equal to n. + * + * If n is already a power of 2, return n. Otherwise, return the next higher power of 2. + * + * @param n Input integer. + * @return Next power of 2 (>= n). + */ + static inline unsigned int nextPowerOf2(unsigned int n) { + if (n == 0) return 1; + + // Check if n is already a power of 2 + if ((n & (n - 1)) == 0) { + return n; + } + + // Otherwise, compute the next higher power of 2 + n--; + n |= n >> 1; + n |= n >> 2; + n |= n >> 4; + n |= n >> 8; + n |= n >> 16; + n++; + return n; + } + + + + /** + * @brief Load numeric data from a CSV file. + * + * Reads a CSV file and converts each value into double. + * Invalid values are replaced with 0.0. + * + * @param fileName Path to the CSV file. + * @return 2D vector of doubles with CSV contents. + */ + static inline vector> loadCSV(const string& fileName) { + std::vector> data; + std::ifstream file(fileName); + + if (!file.is_open()) { + std::cerr << "Error opening file: " << fileName << std::endl; + return data; + } + + std::string line; + while (std::getline(file, line)) { + std::vector row; + std::stringstream ss(line); + std::string cell; + while (std::getline(ss, cell, ',')) { + try { + row.push_back(std::stod(cell)); + } catch (const std::invalid_argument& e) { + std::cerr << "Invalid number: " << cell << std::endl; + row.push_back(0.0); + } + } + data.push_back(row); + } + file.close(); + return data; + } + + /** + * @brief Load bias values from a CSV file. + * + * Extracts the first row of the CSV file as bias values. + * + * @param fileName Path to the CSV file. + * @return 1D vector containing bias values. + */ + static inline vector load_bias(string fileName){ + vector> data = loadCSV(fileName); + vector bias; + for (size_t i = 0; i< data.size(); i++) { + bias = data[0]; + // cout << " bias data: "<< bias << endl; + } + return bias; + } + + /** + * @brief Load and reshape convolution weights from a CSV file. + * + * Reads flat weight data and reshapes into a 4D structure: + * [outputChannels][inputChannels][rowsWidth][imgCols]. + * + * @param fileName Path to the CSV file. + * @param outputChannels Number of output channels. + * @param inputChannels Number of input channels. + * @param rowsWidth Number of rows in each kernel. + * @param imgCols Number of columns in each kernel. + * @return 4D vector containing reshaped weights. + */ + static inline vector>>> load_weights(string fileName, int outputChannels, int inputChannels, + int rowsWidth, int imgCols) { + vector> data = loadCSV(fileName); + vector raw_weights; + vector>>> reshapedData(outputChannels, + vector>>(inputChannels, + vector>(rowsWidth, vector(imgCols)))); + int indexVal = 0; + + for (const auto& row : data) { + raw_weights.insert(raw_weights.end(), row.begin(), row.end()); + } + for(int i = 0; i< outputChannels; i++){ + for(int j=0; j> load_fc_weights(string fileName, int outputChannels, int inputChannels){ + vector> data = loadCSV(fileName); + vector raw_weights; + for (const auto& row : data) { + raw_weights.insert(raw_weights.end(), row.begin(), row.end()); + } + + vector> reshapedData(outputChannels, vector(inputChannels)); + int indexVal = 0; + for(int i = 0; i< outputChannels; i++){ + for(int j=0; j< inputChannels; j++){ + reshapedData[i][j] = raw_weights[indexVal]; + indexVal+=1; + } + } + return reshapedData; + } + + + + /** + * @brief Write text content to a file. + * + * @param filename Path to the output file. + * @param content String content to write. + */ + static inline void write_to_file(string filename, string content) { + ofstream file; + file.open (filename); + file << content.c_str(); + file.close(); + } + + + /** + * @brief Read the first line from a file. + * + * Reads only the first line and returns it as a string. + * + * @param filename Path to the input file. + * @return First line from the file. + */ + static inline string read_from_file(string filename) { + //It reads only the first line!! + string line; + ifstream myfile (filename); + if (myfile.is_open()) { + if (getline(myfile, line)) { + myfile.close(); + return line; + } else { + cerr << "Could not open " << filename << "." < serialize_rotation_keys( vector> rotation_keys){ + + vector rotation_positions; + for (const auto& vec : rotation_keys) { + rotation_positions.insert(rotation_positions.end(), vec.begin(), vec.end()); + } + + std::sort(rotation_positions.begin(), rotation_positions.end()); + rotation_positions.erase(std::unique(rotation_positions.begin(), rotation_positions.end()), rotation_positions.end()); + rotation_positions.erase(std::remove(rotation_positions.begin(), rotation_positions.end(), 0), rotation_positions.end()); + + return rotation_positions; + } + +} + +#endif //FHEON_DATAUTILS_H \ No newline at end of file diff --git a/submissions/cifar10/include/fheon/UtilsImage.h b/submissions/cifar10/include/fheon/UtilsImage.h new file mode 100755 index 0000000..0512af8 --- /dev/null +++ b/submissions/cifar10/include/fheon/UtilsImage.h @@ -0,0 +1,260 @@ + +/*********************************************************************************************************************** +* +* @author: Nges Brian, Njungle +* +* MIT License +* Copyright (c) 2025 Secure, Trusted and Assured Microelectronics, Arizona State University + +* Permission is hereby granted, free of charge, to any person obtaining a copy +* of this software and associated documentation files (the "Software"), to deal +* in the Software without restriction, including without limitation the rights +* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +* copies of the Software, and to permit persons to whom the Software is +* furnished to do so, subject to the following conditions: + +* The above copyright notice and this permission notice shall be included in all +* copies or substantial portions of the Software. + +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +********************************************************************************************************************/ + +/** + * @file ImageInput + * @brief Utilities for inputting images into HE-friendly neural networks. + * + * Provides functions for handling datasets such as CIFAR-10 and MNIST, + * preparing them for use in homomorphic encryption-based neural networks. + */ + +#ifndef FHEON_IMAGESUTILS_H +#define FHEON_IMAGESUTILS_H + +#include +#include + +using namespace std; + +namespace utilsimages { + + /** + * @brief Read CIFAR-10 image data and return multiple images as normalized vectors. + * + * @param full_path Path to binary file. + * @param num_images Number of images to read. + * @param img_size Size of one image (in bytes). + * @return Vector of images, each image stored as a flattened [R,G,B] channel vector. + */ + static inline vector> read_images(string full_path, int num_images, int img_size) { + int img_cols = 32; + vector meanValues = {0.4914, 0.4822, 0.4465}; + vector stdValues = {0.2023, 0.1994, 0.2010}; + ifstream file(full_path, ios::binary); + if (!file.is_open()) { + cerr << "Error opening CIFAR-10 file!" << endl; + return {}; + } + file.seekg(1, std::ios::cur); // Skip 1 byte (label) + // read images + int total_size = img_size * num_images; + vector imagePixels(total_size); + file.read(reinterpret_cast(imagePixels.data()), total_size); + vector> allImages; + + for (int i = 0; i < num_images; ++i) { + int image_offset = i * img_size; + // Separate channels (Red, Green, Blue) into individual vectors + vector redChannel(img_cols * img_cols); + vector greenChannel(img_cols * img_cols); + vector blueChannel(img_cols * img_cols); + + for (int j = 0; j < img_cols * img_cols; ++j) { + double redValue = (static_cast(imagePixels[image_offset + j])); + double greenValue = (static_cast(imagePixels[image_offset + (img_cols * img_cols) + j])); + double blueValue = (static_cast(imagePixels[image_offset + (2 * img_cols * img_cols) + j])); + + redChannel[j] = (((redValue/255.0)- meanValues[0])/stdValues[0]); + greenChannel[j] = (((greenValue/255.0)- meanValues[1])/stdValues[1]); // Green channel + blueChannel[j] = (((blueValue/255.0)- meanValues[2])/stdValues[2]); + } + // Create a single vector to hold all channels in sequence + vector allPixels; + allPixels.insert(allPixels.end(), redChannel.begin(), redChannel.end()); + allPixels.insert(allPixels.end(), greenChannel.begin(), greenChannel.end()); + allPixels.insert(allPixels.end(), blueChannel.begin(), blueChannel.end()); + + // Add the single image vector to the allImages vector + allImages.push_back(allPixels); + } + + file.close(); + return allImages; + } + + /** + * @brief Display pixel values of a single image in channel-wise 32x32 format. + * + * @param allPixels Flattened vector of one image (3 * 32 * 32). + * @param imageSize Size of image vector. + * @param pixelState If true, prints pixel values channel by channel. + */ + static inline void display_image(vector allPixels, int imageSize, bool pixelState){ + int img_cols = 32; + cout << "Image pixel values (3*32x32):" << endl; + + if(pixelState){ + cout << "Image Red Channel:\n"; + for (int j = 0; j < img_cols * img_cols; ++j) { + cout << allPixels[j] << " "; + if ((j + 1) % img_cols == 0) cout << "\n"; + } + + cout << "\nImage Green Channel:\n"; + for (int j = 0; j < img_cols * img_cols; ++j) { + cout << (allPixels[(img_cols * img_cols) + j]) << " "; + if ((j + 1) % img_cols == 0) cout << "\n"; + } + + cout << "\nImage Blue Channel:\n"; + for (int j = 0; j < img_cols * img_cols; ++j) { + cout << (allPixels[(2 * img_cols * img_cols) + j]) << " "; + if ((j + 1) % img_cols == 0) cout << "\n"; + } + } + else{ + cout << endl << endl; + } + std::cout << "Total number of pixels in the combined vector: " << allPixels.size() << std::endl; + } + + /** + * @brief Clear image data from memory. + * + * @param imagesData Vector of images. + * @param numImages Number of images (unused, kept for consistency). + */ + static inline void clear_images(vector> imagesData, int numImages){ + // free the memory + imagesData.clear(); + } + + /** + * @brief Read MNIST images from a binary file into a 2D unsigned char array. + * + * @param full_path Path to MNIST image file (e.g., train-images.idx3-ubyte). + * @param number_of_images Output parameter: number of images read. + * @param image_size Output parameter: number of pixels per image (rows × cols). + * @return Pointer to 2D array of image data [num_images][image_size]. + * @throws runtime_error If file cannot be opened or is invalid. + */ + static inline unsigned char** read_mnist_images(string full_path, int& number_of_images, int& image_size) { + auto reverseInt = [](int i) { + unsigned char c1, c2, c3, c4; + c1 = i & 255, c2 = (i >> 8) & 255, c3 = (i >> 16) & 255, c4 = (i >> 24) & 255; + return ((int)c1 << 24) + ((int)c2 << 16) + ((int)c3 << 8) + c4; + }; + + typedef unsigned char uchar; + + ifstream file(full_path, ios::binary); + + if(file.is_open()) { + int magic_number = 0, n_rows = 0, n_cols = 0; + + file.read((char *)&magic_number, sizeof(magic_number)); + magic_number = reverseInt(magic_number); + + if(magic_number != 2051) throw runtime_error("Invalid MNIST image file!"); + + file.read((char *)&number_of_images, sizeof(number_of_images)), number_of_images = reverseInt(number_of_images); + file.read((char *)&n_rows, sizeof(n_rows)), n_rows = reverseInt(n_rows); + file.read((char *)&n_cols, sizeof(n_cols)), n_cols = reverseInt(n_cols); + + image_size = n_rows * n_cols; + + uchar** _dataset = new uchar*[number_of_images]; + for(int i = 0; i < number_of_images; i++) { + _dataset[i] = new uchar[image_size]; + file.read((char *)_dataset[i], image_size); + } + return _dataset; + } else { + throw runtime_error("Cannot open file `" + full_path + "`!"); + } + } + + /** + * @brief Convert a single MNIST image to a normalized vector of doubles. + * + * Normalization: (pixel / 255.0 - mean) / std. + * Uses mean = 0.1307, std = 0.3081. + * + * @param imageData Pointer to raw image data (unsigned char array). + * @param imageSize Number of pixels (28 × 28). + * @return Normalized image as a vector. + */ + static inline vector read_single_mnist_image(unsigned char* imageData, int imageSize) { + vector imageVector; + double meanVal = 0.1307; // mean value + double stdVal = 0.3081; // standard diviation value + for (int i = 0; i < imageSize; i++) { + auto rescaledVal = static_cast(imageData[i])/ 255.0; + double normalizedVal = (rescaledVal - meanVal) / stdVal; + imageVector.push_back(normalizedVal); + } + return imageVector; + } + + /** + * @brief Display a single MNIST image in 28×28 format. + * + * @param imageData Pointer to raw image data (unsigned char array). + * @param imageSize Number of pixels (28 × 28). + * @param pixelState If true, prints pixel intensity values. If false, prints ASCII visualization ("X" or "."). + */ + static inline void display_mnist_image( unsigned char* imageData, int imageSize, bool pixelState){ + int height = 28; + int width = 28; + cout << "Image pixel values (28x28):" << endl; + if(pixelState){ + for (int i = 0; i < height; i++) { + for (int j = 0; j < width; j++) { + int pixel = imageData[i * width + j]; + cout << (int)pixel << "\t"; + } + cout << endl; + } + cout << endl; + } + else{ + for (int i = 0; i < height; i++) { + for (int j = 0; j < width; j++) { + int pixel = imageData[i * width + j]; + cout << (pixel > 0 ? "X" : ".") << " "; + } + cout << endl; + } + } + } + + /** + * @brief Free memory allocated for MNIST image dataset. + * + * @param mnistData Pointer to 2D array of MNIST images. + * @param numImages Number of images to free. + */ + static inline void clear_mnist_images(unsigned char** mnistData, int numImages){ + for (int i = 0; i < numImages; i++) { + delete[] mnistData[i]; + } + delete[] mnistData; + } + +} + +#endif //FHEON_IMAGESUTILS_H \ No newline at end of file diff --git a/submissions/cifar10/include/params.h b/submissions/cifar10/include/params.h new file mode 100644 index 0000000..cf47d6c --- /dev/null +++ b/submissions/cifar10/include/params.h @@ -0,0 +1,78 @@ +// Copyright 2025 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#ifndef PARAMS_H_ +#define PARAMS_H_ +// params.h - parameters and directory structure for the workload + +#include +#include +#include +#include +namespace fs = std::filesystem; + +// an enum for benchmark size +enum InstanceSize { SINGlE = 0, SMALL = 1, MEDIUM = 2, LARGE = 3 }; +inline std::string instance_name(const InstanceSize size) { + if (unsigned(size) > unsigned(InstanceSize::LARGE)) { + return "unknown"; + } + static const std::string names[] = {"single", "small", "medium", "large"}; + return names[int(size)]; +} + +// Parameters that differ for different instance sizes +class InstanceParams { + const InstanceSize size; + size_t batchSize; + // Add any parameters necessary + fs::path rootdir; // root of the submission dir structure (see below) + +public: + // Constructor + explicit InstanceParams(InstanceSize _size, + fs::path _rootdir = fs::current_path()) + : size(_size), rootdir(_rootdir) { + if (unsigned(_size) > unsigned(InstanceSize::LARGE)) { + throw std::invalid_argument("Invalid instance size"); + } + + const int batchSizes[] = {1, 100, 1000, 10000}; + batchSize = batchSizes[int(_size)]; + } + + // Getters for all the parameters. There are no setters, once + // an object is constructed these parameters cannot be modified. + const InstanceSize getSize() const { return size; } + const size_t getBatchSize() const { return batchSize; } + + // The relevant directories where things are found + fs::path rtdir() const { return rootdir; } + fs::path iodir() const { return rootdir / "io" / instance_name(size); } + fs::path pubkeydir() const { return iodir() / "public_keys"; } + fs::path seckeydir() const { return iodir() / "secret_key"; } + fs::path ctxtupdir() const { return iodir() / "ciphertexts_upload"; } + fs::path ctxtdowndir() const { return iodir() / "ciphertexts_download"; } + fs::path iointermdir() const { return iodir() / "intermediate"; } + fs::path datadir() const { + return rootdir / "datasets" / instance_name(size); + } + fs::path dataintermdir() const { return datadir() / "intermediate"; } + fs::path test_input_file() const { return dataintermdir() / "test_pixels.txt"; } + // fs::path test_input_file() const { return datadir()/"dataset_pixels.txt"; } + fs::path encrypted_model_predictions_file() const { + return iodir() / "encrypted_model_predictions.txt"; + } +}; + +#endif // ifndef PARAMS_H_ diff --git a/submissions/cifar10/include/resnet20_fheon.h b/submissions/cifar10/include/resnet20_fheon.h new file mode 100644 index 0000000..afc263d --- /dev/null +++ b/submissions/cifar10/include/resnet20_fheon.h @@ -0,0 +1,41 @@ +// Copyright 2025 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#ifndef RESNET20_FHEON_H_ +#define RESNET20_FHEON_H_ + +#include "FHEONANNController.h" +#include "FHEONHEController.h" +#include "openfhe.h" + +using namespace std; +using namespace lbcrypto; + +struct ResNet20Config { + vector levelBudget = {3, 3}; + vector bsgsDim = {0, 0}; + int ringDim = 1 << 15; + int numSlots = 1 << 14; + int dcrtBits = 48; + int firstMod = 52; + int modelDepth = 11; + int digitSize = 4; +}; + +inline ResNet20Config config; + +// using CiphertextT = ConstCiphertext; + +Ctext resnet20(FHEONHEController &fheonHEController, CryptoContext &v0, Ctext &v1, string pubkey_dir); + +#endif // ifndef RESNET20_FHEON_H_ \ No newline at end of file diff --git a/submissions/cifar10/include/utils.h b/submissions/cifar10/include/utils.h new file mode 100644 index 0000000..73d0103 --- /dev/null +++ b/submissions/cifar10/include/utils.h @@ -0,0 +1,29 @@ +// Copyright 2025 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef UTILS_H +#define UTILS_H + +#include "openfhe.h" +// header files needed for de/serialization +#include "ciphertext-ser.h" +#include "cryptocontext-ser.h" +#include "key/key-ser.h" +#include "scheme/ckksrns/ckksrns-ser.h" + +using namespace lbcrypto; + +// Placeholder + +#endif // __UTILS_H__ \ No newline at end of file diff --git a/submissions/cifar10/src/client_decrypt_decode.cpp b/submissions/cifar10/src/client_decrypt_decode.cpp new file mode 100644 index 0000000..4fc62de --- /dev/null +++ b/submissions/cifar10/src/client_decrypt_decode.cpp @@ -0,0 +1,60 @@ +// Copyright 2025 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#include "iomanip" +#include "limits" +#include "utils.h" + +#include "encryption_utils.h" + +using namespace lbcrypto; + +int main(int argc, char *argv[]) { + if (argc < 2 || !std::isdigit(argv[1][0])) { + std::cout << "Usage: " << argv[0] << " instance-size [--count_only]\n"; + std::cout << " Instance-size: 0-SINGLE, 1-SMALL, 2-MEDIUM, 3-LARGE\n"; + return 0; + } + auto size = static_cast(std::stoi(argv[1])); + InstanceParams prms(size); + + CryptoContext cc; + if (!Serial::DeserializeFromFile(prms.pubkeydir() / "cc.bin", cc, + SerType::BINARY)) { + throw std::runtime_error("Failed to get CryptoContext from " + + prms.pubkeydir().string()); + } + PrivateKey sk; + if (!Serial::DeserializeFromFile(prms.seckeydir() / "sk.bin", sk, + SerType::BINARY)) { + throw std::runtime_error("Failed to get secret key from " + + prms.seckeydir().string()); + } + Ciphertext ctxt; + std::vector output; + auto result_path = prms.encrypted_model_predictions_file(); + std::ofstream out(result_path); + for (size_t i = 0; i < prms.getBatchSize(); ++i) { + auto ctxt_path = + prms.ctxtdowndir() / ("cipher_result_" + std::to_string(i) + ".bin"); + if (!Serial::DeserializeFromFile(ctxt_path, ctxt, SerType::BINARY)) { + throw std::runtime_error("Failed to get ciphertext from " + + ctxt_path.string()); + } + output = input_decrypt(cc, ctxt, sk); + auto max_id = argmax(output.data(), 1024); + out << max_id << '\n'; + } + + return 0; +} diff --git a/submissions/cifar10/src/client_encode_encrypt_input.cpp b/submissions/cifar10/src/client_encode_encrypt_input.cpp new file mode 100644 index 0000000..1413f13 --- /dev/null +++ b/submissions/cifar10/src/client_encode_encrypt_input.cpp @@ -0,0 +1,72 @@ +// Copyright 2025 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#include "encryption_utils.h" +#include "utils.h" + +using namespace lbcrypto; + +int main(int argc, char *argv[]) { + + if (argc < 2 || !std::isdigit(argv[1][0])) { + std::cout << "Usage: " << argv[0] << " instance-size [--count_only]\n"; + std::cout << " Instance-size: 0-SINGLE, 1-SMALL, 2-MEDIUM, 3-LARGE\n"; + return 0; + } + auto size = static_cast(std::stoi(argv[1])); + InstanceParams prms(size); + + CryptoContext cc = read_crypto_context(prms); + + // Step 2: Read public key + PublicKey pk = read_public_key(prms); + + std::vector dataset; + load_dataset(dataset, prms.test_input_file().c_str(), CIFAR_DIM, prms.getBatchSize()); + if (dataset.empty()) { + throw std::runtime_error("No data found in " + prms.test_input_file().string()); + } + // Step 2: Encrypt inputs + if (dataset.size() != prms.getBatchSize()) { + throw std::runtime_error("Dataset size does not match instance size"); + } + + std::shared_ptr> ctxt; + fs::create_directories(prms.ctxtupdir()); + for (size_t i = 0; i < dataset.size(); ++i) { + // for(size_t i = 0; i < 1; ++i) { + auto *input = dataset[i].image; + // std::vector input_vector(input, input + NORMALIZED_DIM); + std::vector input_vector(input, input + CIFAR_DIM); + // Apply CIFAR10 per-channel normalization: (x - mean) / std + // CIFAR10: mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010] + float cifar10_mean[] = {0.4914f, 0.4822f, 0.4465f}; + float cifar10_std[] = {0.2023f, 0.1994f, 0.2010f}; + int pixels_per_channel = CIFAR_DIM / 3; + // std::cout << "Plaintext sample: " << input_vector << "..." << std::endl; + + for (int c = 0; c < 3; ++c) { + for (int idx = c * pixels_per_channel; idx < (c + 1) * pixels_per_channel; ++idx) { + input_vector[idx] = ((input_vector[idx]) - cifar10_mean[c]) / cifar10_std[c]; + } + } + + // std::cout << "Encrypting sample: " << std::endl + // << input_vector << std::endl; + ctxt = input_encrypt(cc, input_vector, pk); + auto ctxt_path = prms.ctxtupdir() / ("cipher_input_" + std::to_string(i) + ".bin"); + Serial::SerializeToFile(ctxt_path, ctxt, SerType::BINARY); + } + + return 0; +} diff --git a/submissions/cifar10/src/client_key_generation.cpp b/submissions/cifar10/src/client_key_generation.cpp new file mode 100644 index 0000000..c7665b2 --- /dev/null +++ b/submissions/cifar10/src/client_key_generation.cpp @@ -0,0 +1,228 @@ +// Copyright 2025 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "encryption_utils.h" +#include "resnet20_fheon.h" + +#include "utils.h" +#include +#include +#include +#include +#include +#include + +using namespace std; +namespace fs = std::filesystem; + +// Parameters are now defined in resnet20_fheon.h via config struct + +CryptoContextT generate_crypto_context() { + + lbcrypto::SecretKeyDist secretKeyDist = lbcrypto::SPARSE_TERNARY; + int circuitDepth = config.modelDepth + + lbcrypto::FHECKKSRNS::GetBootstrapDepth(config.levelBudget, + secretKeyDist); + + CCParamsT parameters; + parameters.SetMultiplicativeDepth(circuitDepth); + parameters.SetSecurityLevel(HEStd_128_classic); + // parameters.SetSecurityLevel(HEStd_NotSet); + // parameters.SetRingDim(config.ringDim); + // parameters.SetBatchSize(config.numSlots); + parameters.SetScalingModSize(config.dcrtBits); + parameters.SetFirstModSize(config.firstMod); + parameters.SetNumLargeDigits(config.digitSize); + parameters.SetScalingTechnique(FLEXIBLEAUTO); + parameters.SetSecretKeyDist(secretKeyDist); + + CryptoContextT context = GenCryptoContext(parameters); + context->Enable(PKE); + context->Enable(KEYSWITCH); + context->Enable(LEVELEDSHE); + context->Enable(ADVANCEDSHE); + context->Enable(FHE); + + cout << "Context built, generating keys..." << endl; + cout << endl + << "dcrtBits: " << config.dcrtBits << " -- firstMod: " << config.firstMod + << endl + << "Ciphertexts depth: " << circuitDepth + << ", available multiplications: " << config.modelDepth - 2 << endl; + + return context; +} + +CryptoContextT generate_mult_rot_key(CryptoContextT context, PrivateKeyT secretKey) { + + context->EvalMultKeyGen(secretKey); + vector rotPositions = { + // -3276, // required for ringRim 1 << 16 + -15360, -14336, -13312, -12288, -11520, -11264, -10240, -9216, -8192, + -7936, -7680, -7424, -7168, -6912, -6656, -6400, -6144, -5952, + -5888, -5632, -5376, -5120, -4864, -4608, -4352, -4096, -4032, + -3968, -3904, -3840, -3776, -3712, -3648, -3584, -3520, -3456, + -3392, -3328, -3264, -3200, -3136, -3072, -3008, -2944, -2880, + -2816, -2752, -2688, -2624, -2560, -2496, -2432, -2368, -2304, + -2240, -2176, -2112, -2048, -1984, -1920, -1856, -1792, -1728, + -1664, -1600, -1536, -1472, -1408, -1344, -1280, -1216, -1152, + -1088, -1024, -960, -896, -832, -768, -704, -640, -576, + -512, -448, -384, -320, -256, -192, -128, -64, -48, + -32, -16, -8, -1, 1, 2, 3, 4, 5, + 6, 7, 8, 9, 10, 11, 12, 13, 14, + 15, 16, 24, 32, 48, 64, 256, 1024}; + context->EvalRotateKeyGen(secretKey, rotPositions); + return context; +} + +void generate_rotation_keys(FHEONHEController &fheonHEController, + CryptoContextT context, PrivateKeyT secretKey, + vector channels, int dataset_size) { + + FHEONANNController fheonANNController(context); + + int img_depth = 3; + int dataWidth = 32; + int avgpoolSize = 8; + int rotPositions = 16; + auto size = static_cast(dataset_size); + InstanceParams prms(size); + + /************************************** generate rotation keys for conv_layer + * 1 **********************************8*/ + auto conv1_keys = fheonANNController.generate_optimized_convolution_rotation_positions(dataWidth, img_depth, channels[0]); + auto conv2_keys = fheonANNController.generate_optimized_convolution_rotation_positions(dataWidth, channels[0], channels[0]); + auto conv3_keys = fheonANNController.generate_optimized_convolution_rotation_positions(dataWidth, channels[0], channels[1], 2, "single_channel"); + dataWidth = dataWidth / 2; + + auto conv4_keys = fheonANNController.generate_optimized_convolution_rotation_positions(dataWidth, channels[1], channels[1]); + auto conv5_keys = fheonANNController.generate_optimized_convolution_rotation_positions(dataWidth, channels[1], channels[2], 2, "single_channel"); + dataWidth = dataWidth / 2; + + auto conv6_keys = fheonANNController.generate_optimized_convolution_rotation_positions(dataWidth, channels[2], channels[2]); + auto avgpool1_key = fheonANNController.generate_avgpool_optimized_rotation_positions(dataWidth, channels[2], avgpoolSize, avgpoolSize, true, + "single_channel", rotPositions); + auto fc_keys = fheonANNController.generate_linear_rotation_positions(channels[3], rotPositions); + + /*************************************************************************************************/ + vector> rkeys_layer1, rkeys_layer2, rkeys_layer3, rkeys_layer4; + rkeys_layer1.push_back(conv1_keys); + rkeys_layer1.push_back(conv2_keys); + + rkeys_layer2.push_back(conv3_keys); + rkeys_layer2.push_back(conv4_keys); + + rkeys_layer3.push_back(conv5_keys); + rkeys_layer3.push_back(conv6_keys); + + rkeys_layer4.push_back(avgpool1_key); + rkeys_layer4.push_back(fc_keys); + // rkeys_layer4.push_back({32, 64}); + + /********************************************************************************************************************************************/ + /*** join all keys and generate unique values only */ + vector serkeys_layer1 = serialize_rotation_keys(rkeys_layer1); + vector serkeys_layer2 = serialize_rotation_keys(rkeys_layer2); + vector serkeys_layer3 = serialize_rotation_keys(rkeys_layer3); + vector serkeys_layer4 = serialize_rotation_keys(rkeys_layer4); + + cout << "Layer 1 keys (" << serkeys_layer1.size() << ") " << serkeys_layer1 << endl; + cout << "Layer 2 keys (" << serkeys_layer2.size() << ") " << serkeys_layer2 << endl; + cout << "Layer 3 keys (" << serkeys_layer3.size() << ") " << serkeys_layer3 << endl; + cout << "Layer 4 keys (" << serkeys_layer4.size() << ") " << serkeys_layer4 << endl; + + ofstream layer1_file(prms.pubkeydir() / "layer1_rk.bin", ios::out | ios::binary); + ofstream layer2_file(prms.pubkeydir() / "layer2_rk.bin", ios::out | ios::binary); + ofstream layer3_file(prms.pubkeydir() / "layer3_rk.bin", ios::out | ios::binary); + ofstream layer4_file(prms.pubkeydir() / "layer4_rk.bin", ios::out | ios::binary); + + // Each layer's file must include bootstrap automorphism keys so the server + // can call EvalBootstrap without sk. Pattern per layer: + fheonHEController.harness_generate_bootstrapping_and_rotation_keys(context, secretKey, serkeys_layer1, layer1_file); + fheonHEController.harness_clear_bootstrapping_and_rotation_keys(context); + + fheonHEController.harness_generate_bootstrapping_and_rotation_keys(context, secretKey, serkeys_layer2, layer2_file); + fheonHEController.harness_clear_bootstrapping_and_rotation_keys(context); + + fheonHEController.harness_generate_bootstrapping_and_rotation_keys(context, secretKey, serkeys_layer3, layer3_file); + fheonHEController.harness_clear_bootstrapping_and_rotation_keys(context); + + fheonHEController.harness_generate_bootstrapping_and_rotation_keys(context, secretKey, serkeys_layer4, layer4_file, true); + fheonHEController.harness_clear_bootstrapping_and_rotation_keys(context); + cout << "All keys generated" << endl; + /********************************************************************************************************************************************/ +} + +int main(int argc, char *argv[]) { + + if (argc < 2 || !isdigit(argv[1][0])) { + cout << "Usage: " << argv[0] << " instance-size [--count_only]\n"; + cout << " Instance-size: 0-SINGLE, 1-SMALL, 2-MEDIUM, 3-LARGE\n"; + return 0; + } + + int dataset_size = stoi(argv[1]); + auto size = static_cast(dataset_size); + InstanceParams prms(size); + + // Step 1: Setup CryptoContext + auto cryptoContext = generate_crypto_context(); + + // Step 2: Key Generation + // cout << "Starting KeyGen..." << endl; + auto keyPair = cryptoContext->KeyGen(); + cryptoContext->EvalMultKeyGen(keyPair.secretKey); + cryptoContext->EvalSumKeyGen(keyPair.secretKey); + + FHEONHEController fheonHEController(cryptoContext); + double logPQ = fheonHEController.getlogPQ(keyPair.publicKey->GetPublicElements()[0]); + cout << "log PQ = " << logPQ << std::endl; + cout << "Cyclotomic Order: " << cryptoContext->GetCyclotomicOrder() << endl; + cout << "Ring dimension: " << (cryptoContext->GetCyclotomicOrder() / 2) << endl; + cout << "Num Slots : " << (cryptoContext->GetCyclotomicOrder() / 4) << endl; + cout << endl; + + // Step 3: Serialize cryptocontext and keys + fs::create_directories(prms.pubkeydir()); + // cout << "Serializing CC and PK..." << endl; + + if (!Serial::SerializeToFile(prms.pubkeydir() / "cc.bin", cryptoContext, + SerType::BINARY) || + !Serial::SerializeToFile(prms.pubkeydir() / "pk.bin", keyPair.publicKey, + SerType::BINARY)) { + throw runtime_error("Failed to write keys to " + prms.pubkeydir().string()); + } + // cout << "CC and PK serialized. Serializing Eval Keys..." << endl; + ofstream emult_file(prms.pubkeydir() / "mk.bin", ios::out | ios::binary); + // ofstream erot_file(prms.pubkeydir() / "rk.bin", ios::out | ios::binary); + if (!emult_file.is_open() || + !cryptoContext->SerializeEvalMultKey(emult_file, SerType::BINARY)) { + throw runtime_error("Failed to write mult keys to " + + prms.pubkeydir().string()); + } + + /*** work on rotation keys */ + vector channels = {16, 32, 64, 10}; + generate_rotation_keys(fheonHEController, cryptoContext, keyPair.secretKey, channels, dataset_size); + // cout << "Eval Keys serialized. Serializing Secret Key..." << endl; + + fs::create_directories(prms.seckeydir()); + if (!Serial::SerializeToFile(prms.seckeydir() / "sk.bin", keyPair.secretKey, + SerType::BINARY)) { + throw runtime_error("Failed to write keys to " + prms.seckeydir().string()); + } + // cout << "Secret Key serialized." << endl; + + return 0; +} diff --git a/submissions/cifar10/src/client_postprocess.cpp b/submissions/cifar10/src/client_postprocess.cpp new file mode 100644 index 0000000..2a8ba05 --- /dev/null +++ b/submissions/cifar10/src/client_postprocess.cpp @@ -0,0 +1,10 @@ +// Copyright (c) 2025 HomomorphicEncryption.org +// All rights reserved. +// +// This software is licensed under the terms of the Apache v2 License. +// See the LICENSE.md file for details. +//============================================================================ + +int main(){ + return 0; +} \ No newline at end of file diff --git a/submissions/cifar10/src/client_preprocess_input.cpp b/submissions/cifar10/src/client_preprocess_input.cpp new file mode 100644 index 0000000..a5ad894 --- /dev/null +++ b/submissions/cifar10/src/client_preprocess_input.cpp @@ -0,0 +1,10 @@ +// Copyright (c) 2025 HomomorphicEncryption.org +// All rights reserved. +// +// This software is licensed under the terms of the Apache v2 License. +// See the LICENSE.md file for details. +//============================================================================ + +int main(){ + return 0; +} \ No newline at end of file diff --git a/submissions/cifar10/src/encryption_utils.cpp b/submissions/cifar10/src/encryption_utils.cpp new file mode 100644 index 0000000..1581f1a --- /dev/null +++ b/submissions/cifar10/src/encryption_utils.cpp @@ -0,0 +1,139 @@ +// Copyright 2025 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. +#include "encryption_utils.h" +#include "utils.h" +#include +#include + +PublicKey read_public_key(const InstanceParams &prms) { + PublicKey pk; + if (!Serial::DeserializeFromFile(prms.pubkeydir() / "pk.bin", pk, + SerType::BINARY)) { + throw std::runtime_error("Failed to get public key from " + + prms.pubkeydir().string()); + } + return pk; +} + +PrivateKey read_secret_key(const InstanceParams &prms) { + PrivateKey sk; + if (!Serial::DeserializeFromFile(prms.seckeydir() / "sk.bin", sk, + SerType::BINARY)) { + throw std::runtime_error("Failed to get secret key from " + + prms.seckeydir().string()); + } + return sk; +} + +CryptoContextT read_crypto_context(const InstanceParams &prms) { + CryptoContextT cc; + if (!Serial::DeserializeFromFile(prms.pubkeydir() / "cc.bin", cc, + SerType::BINARY)) { + throw std::runtime_error("Failed to get CryptoContext from " + + prms.pubkeydir().string()); + } + return cc; +} + +void read_eval_keys(const InstanceParams &prms, CryptoContextT cc) { + std::ifstream emult_file(prms.pubkeydir() / "mk.bin", + std::ios::in | std::ios::binary); + if (!emult_file.is_open() || + !cc->DeserializeEvalMultKey(emult_file, SerType::BINARY)) { + throw std::runtime_error("Failed to get re-linearization key from " + + prms.pubkeydir().string()); + } + + std::ifstream erot_file(prms.pubkeydir() / "rk.bin", + std::ios::in | std::ios::binary); + if (!erot_file.is_open() || + !cc->DeserializeEvalAutomorphismKey(erot_file, SerType::BINARY)) { + throw std::runtime_error("Failed to get rotation keys from " + + prms.pubkeydir().string()); + } +} + +ConstCiphertext input_encrypt(CryptoContext cc, + std::vector input, + PublicKey pk) { + std::vector v11340(std::begin(input), std::end(input)); + uint32_t v11340_filled_n = + cc->GetCryptoParameters()->GetElementParams()->GetRingDimension() / 2; + auto v11340_filled = v11340; + v11340_filled.clear(); + v11340_filled.reserve(v11340_filled_n); + for (uint32_t i = 0; i < v11340_filled_n; ++i) { + v11340_filled.push_back(v11340[i % v11340.size()]); + } + const auto &v11341 = cc->MakeCKKSPackedPlaintext(v11340_filled); + const auto &v11342 = cc->Encrypt(pk, v11341); + + // auto plaintext = cc->MakeCKKSPackedPlaintext(v11340, 1, 1); + // plaintext->SetLength(input.size()); + return v11342; +} + +std::vector input_decrypt(CryptoContextT v11343, CiphertextT v11344, + PrivateKeyT v11345) { + PlaintextT v11346; + v11343->Decrypt(v11345, v11344, &v11346); + // v11346->SetLength(1024); + const auto &v11347_cast = v11346->GetCKKSPackedValue(); + std::vector v11347(v11347_cast.size()); + std::transform(std::begin(v11347_cast), std::end(v11347_cast), + std::begin(v11347), + [](const std::complex &c) { return c.real(); }); + return v11347; +} + +void load_dataset(std::vector &dataset, const char *filename, int dim, + int max_samples) { + std::ifstream file(filename); + if (!file.is_open()) { + throw std::runtime_error(std::string("Failed to open file: ") + filename); + } + Sample sample; + std::string line; + while (std::getline(file, line)) { + std::istringstream iss(line); + // Read dim values from file + for (int i = 0; i < dim; i++) { + if (!(iss >> sample.image[i])) { + throw std::runtime_error("Error: Failed to read value at index " + std::to_string(i) + + " from line. Expected " + std::to_string(dim) + + " values, but got fewer. Please ensure you generated the " + "correct dataset (e.g., using --dataset cifar10 instead of mnist)."); + } + } + // Pad remaining values with 0.0 if NORMALIZED_DIM > dim + for (int i = dim; i < NORMALIZED_DIM; i++) { + sample.image[i] = 0.0f; + } + + dataset.push_back(sample); + if (max_samples > 0 && static_cast(dataset.size()) >= max_samples) { + break; + } + } +} + +int argmax(float *A, int N) { + int max_idx = 0; + for (int i = 1; i < N; i++) { + if (A[i] > A[max_idx]) { + max_idx = i; + } + } + return max_idx; +} diff --git a/submissions/cifar10/src/resnet20_fheon.cpp b/submissions/cifar10/src/resnet20_fheon.cpp new file mode 100755 index 0000000..1c9ed92 --- /dev/null +++ b/submissions/cifar10/src/resnet20_fheon.cpp @@ -0,0 +1,256 @@ + +/*********************************************************************************************************************** +* +* @author: Nges Brian, Njungle +* +* MIT License +* Copyright (c) 2025 Secure, Trusted and Assured Microelectronics, Arizona State +University + +* Permission is hereby granted, free of charge, to any person obtaining a copy +* of this software and associated documentation files (the "Software"), to deal +* in the Software without restriction, including without limitation the rights +* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +* copies of the Software, and to permit persons to whom the Software is +* furnished to do so, subject to the following conditions: + +* The above copyright notice and this permission notice shall be included in all +* copies or substantial portions of the Software. + +* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +********************************************************************************************************************/ + +#include "resnet20_fheon.h" +#include +#include + +using namespace std; +using namespace lbcrypto; + +#ifndef WEIGHTS_DIR +#define WEIGHTS_DIR "./submissions/cifar10/resnet20/weights/resnet20/" +#endif + +Ctext convolution_block(FHEONHEController &fheonHEController, FHEONANNController &fheonANNController, string layer, Ctext &encrytedInput, + int &dataWidth, int kernelWidth, int striding, int inputChannels, int outputChannels); +vector double_shortcut_convolution_block(FHEONHEController &fheonHEController, FHEONANNController &fheonANNController, string layer, + Ctext &encrytedInput, int &dataWidth, int inputChannels, int outputChannels); +Ctext resnet_block(FHEONHEController &fheonHEController, FHEONANNController &fheonANNController, string layer, Ctext &encrytedInput, + int &dataWidth, int &dataSize, int inputChannels, int outputChannels, int bootstrapState, bool shortcutConv); +Ctext fc_layer_block(FHEONHEController &fheonHEController, FHEONANNController &fheonANNController, string layer, + Ctext encryptedInput, int inputChannels, int outputChannels); + +Ctext resnet20(FHEONHEController &fheonHEController, CryptoContext &context, Ctext &encryptedInput, string pubkey_dir) { + + FHEONANNController fheonANNController(context); + + int img_depth = 3; + int img_cols = 32; + int kernelSize = 3; + int striding = 1; + int avgpoolSize = 8; + vector channelValues = {16, 32, 64, 10}; + int rotPositions = 16; + int dataWidth = img_cols; + int dataSize = img_depth * pow(img_cols, 2); + int reluScale = 10; + int polyDeg = 59; + + cout << " [server] Starting encrypted ResNet20 inference" << endl; + + string mk_file = "mk.bin"; + cout << " [server] Layer 0" << endl; + string l1_rk = "layer1_rk.bin"; + fheonHEController.read_evaluation_keys(context, pubkey_dir, mk_file, l1_rk); + context->EvalBootstrapSetup(config.levelBudget); + Ctext convData = convolution_block(fheonHEController, fheonANNController, "layer0_conv1", encryptedInput, + dataWidth, kernelSize, striding, img_depth, channelValues[0]); + dataSize = channelValues[0] * pow(dataWidth, 2); + convData = fheonANNController.he_relu(convData, reluScale, dataSize, polyDeg); + + cout << " [server] Layer 1" << endl; + cout << " [server] Block 1" << endl; + convData = resnet_block(fheonHEController, fheonANNController,"layer1_block1", convData, dataWidth, dataSize, + channelValues[0], channelValues[0], false, false); + cout << " [server] Block 2" << endl; + convData = resnet_block(fheonHEController, fheonANNController, "layer1_block2", convData, dataWidth, dataSize, + channelValues[0], channelValues[0], true, false); + cout << " [server] Block 3" << endl; + convData = resnet_block(fheonHEController, fheonANNController, "layer1_block3", convData, dataWidth, dataSize, + channelValues[0], channelValues[0], true, false); + + string l2_rk = "layer2_rk.bin"; + fheonHEController.read_evaluation_keys(context, pubkey_dir, mk_file, l2_rk); + cout << " [server] Layer 2" << endl; + cout << " [server] Block 1" << endl; + convData = resnet_block(fheonHEController, fheonANNController, "layer2_block1", convData, dataWidth, dataSize, + channelValues[0], channelValues[1], true, true); + cout << " [server] Block 2" << endl; + convData = resnet_block(fheonHEController, fheonANNController, "layer2_block2", convData, dataWidth, dataSize, + channelValues[1], channelValues[1], true, false); + cout << " [server] Block 3" << endl; + convData = resnet_block(fheonHEController, fheonANNController, "layer2_block3", convData, dataWidth, dataSize, + channelValues[1], channelValues[1], true, false); + + string l3_rk = "layer3_rk.bin"; + fheonHEController.read_evaluation_keys(context, pubkey_dir, mk_file, l3_rk); + cout << " [server] Layer 3" << endl; + cout << " [server] Block 1" << endl; + convData = resnet_block(fheonHEController, fheonANNController, "layer3_block1", convData, dataWidth, dataSize, + channelValues[1], channelValues[2], true, true); + cout << " [server] Block 2" << endl; + convData = resnet_block(fheonHEController, fheonANNController, "layer3_block2", convData, dataWidth, dataSize, + channelValues[2], channelValues[2], true, false); + cout << " [server] Block 3" << endl; + convData = resnet_block(fheonHEController, fheonANNController, "layer3_block3", convData, dataWidth, dataSize, + channelValues[2], channelValues[2], true, false); + + string l4_rk = "layer4_rk.bin"; + fheonHEController.read_evaluation_keys(context, pubkey_dir, mk_file, l4_rk); + cout << " [server] Pool + Classifier" << endl; + convData = fheonHEController.bootstrap_function(convData); + convData = fheonANNController.he_globalavgpool( convData, dataWidth, channelValues[2], avgpoolSize, rotPositions); + convData = fc_layer_block(fheonHEController, fheonANNController, "layer_fc", convData, channelValues[2], channelValues[3]); + return convData; +} + +Ctext convolution_block(FHEONHEController &fheonHEController, FHEONANNController &fheonANNController, string layer, Ctext &encrytedInput, + int &dataWidth, int kernelWidth, int striding, int inputChannels, int outputChannels) { + + int widthSq = pow(dataWidth, 2); + int encodeLevel = encrytedInput->GetLevel(); + vector> kernelData; + string dataPath = string(WEIGHTS_DIR) + layer; + + auto biasData = load_bias(dataPath + "_bias.csv"); + auto rawKernel = load_weights(dataPath + "_weight.csv", outputChannels, + inputChannels, kernelWidth, kernelWidth); + for (int i = 0; i < outputChannels; i++) { + auto encodeKernel = fheonHEController.encode_kernel_optimized(rawKernel[i], widthSq, encodeLevel); + kernelData.push_back(encodeKernel); + } + + auto biasDataEncoded = fheonHEController.encode_bais_input(biasData, widthSq, encodeLevel); + auto conv_data = fheonANNController.he_convolution_optimized(encrytedInput, kernelData, + biasDataEncoded, dataWidth, inputChannels, outputChannels, striding); + + kernelData.clear(); + kernelData.shrink_to_fit(); + biasData.clear(); + rawKernel.clear(); + rawKernel.shrink_to_fit(); + + return conv_data; +} + +vector double_shortcut_convolution_block(FHEONHEController &fheonHEController, FHEONANNController &fheonANNController, + string layer, Ctext &encrytedInput, int &dataWidth, int inputChannels, int outputChannels) { + + string dataPath = string(WEIGHTS_DIR) + layer; + int widthSq = pow(dataWidth, 2); + int widthOutSq = pow((dataWidth / 2), 2); + int kernelWidth = 3; + int encodeLevel = encrytedInput->GetLevel(); + vector> kernelData; + vector shortcutkernelData; + + /*** convolution and shortcut data */ + auto biasData = load_bias(dataPath + "_conv1_bias.csv"); + auto shortcutbiasData = load_bias(dataPath + "_shortcut_bias.csv"); + auto rawKernel = load_weights(dataPath + "_conv1_weight.csv", outputChannels, + inputChannels, kernelWidth, kernelWidth); + auto shortcutrawKernel = load_fc_weights(dataPath + "_shortcut_weight.csv", + outputChannels, inputChannels); + for (int i = 0; i < outputChannels; i++) { + auto encodeKernel = fheonHEController.encode_kernel_optimized(rawKernel[i], widthSq, encodeLevel); + auto encodeWeights = fheonHEController.encode_bais_input(shortcutrawKernel[i], widthSq); + kernelData.push_back(encodeKernel); + shortcutkernelData.push_back(encodeWeights); + } + auto biasDataEncoded = fheonHEController.encode_bais_input(biasData, widthOutSq); + auto shortcutbiasDataEncoded = fheonHEController.encode_bais_input(shortcutbiasData, widthOutSq); + + auto returnedCiphers = fheonANNController.he_convolution_and_shortcut_optimized( + encrytedInput, kernelData, shortcutkernelData, biasDataEncoded, + shortcutbiasDataEncoded, dataWidth, inputChannels, outputChannels); + + kernelData.clear(); + kernelData.shrink_to_fit(); + rawKernel.clear(); + rawKernel.shrink_to_fit(); + biasData.clear(); + + shortcutkernelData.clear(); + shortcutkernelData.shrink_to_fit(); + shortcutrawKernel.clear(); + shortcutrawKernel.shrink_to_fit(); + shortcutbiasData.clear(); + return returnedCiphers; +} + +Ctext resnet_block(FHEONHEController &fheonHEController, FHEONANNController &fheonANNController, string layer, Ctext &encrytedInput, + int &dataWidth, int &dataSize, int inputChannels, int outputChannels, int bootstrapState, bool shortcutConv) { + + int kernelWidth = 3; + int striding = 1; + int polyDeg = 59; + int reluScale = 15; + Ctext convData; + Ctext shortcutConvData = encrytedInput->Clone(); + + if (shortcutConv) { + encrytedInput = fheonHEController.bootstrap_function(encrytedInput); + auto doubleResults = double_shortcut_convolution_block(fheonHEController, fheonANNController, layer, encrytedInput, dataWidth, + inputChannels, outputChannels); + dataWidth = dataWidth / 2; + dataSize = (outputChannels * pow(dataWidth, 2)); + convData = doubleResults[0]->Clone(); + shortcutConvData = doubleResults[1]->Clone(); + } else { + convData = convolution_block(fheonHEController, fheonANNController, layer + "_conv1", encrytedInput, + dataWidth, kernelWidth, striding, inputChannels, outputChannels); + } + if (bootstrapState) { + convData = fheonHEController.bootstrap_function(convData); + } + + convData = fheonHEController.bootstrap_function(convData); + convData = fheonANNController.he_relu(convData, reluScale, dataSize, polyDeg); + Ctext secConvData = convolution_block(fheonHEController, fheonANNController, layer + "_conv2", convData, + dataWidth, kernelWidth, striding, outputChannels, outputChannels); + Ctext sumConvData = fheonANNController.he_sum_two_ciphertexts(secConvData, shortcutConvData); + sumConvData = fheonHEController.bootstrap_function(sumConvData); + if (layer == "layer3_block2" || layer == "layer3_block3") { + reluScale = 25; + } + sumConvData = fheonANNController.he_relu(sumConvData, reluScale, dataSize, polyDeg); + return sumConvData; +} + +Ctext fc_layer_block(FHEONHEController &fheonHEController, FHEONANNController &fheonANNController, string layer, + Ctext encryptedInput, int inputChannels, int outputChannels) { + + string dataPath = string(WEIGHTS_DIR) + layer; + auto fcBiasData = load_bias(dataPath + "_bias.csv"); + auto fc_rawKernelData = load_fc_weights(dataPath + "_weight.csv", outputChannels, inputChannels); + vector fcKernelData; + for (int i = 0; i < outputChannels; i++) { + auto encodeWeights = fheonHEController.encode_input(fc_rawKernelData[i]); + fcKernelData.push_back(encodeWeights); + } + Ptext encodedbaisData = fheonHEController.encode_input(fcBiasData); + Ctext fcData = fheonANNController.he_linear_optimized(encryptedInput, fcKernelData, encodedbaisData, inputChannels, outputChannels); + + fcKernelData.clear(); + fcKernelData.shrink_to_fit(); + fcBiasData.clear(); + fc_rawKernelData.clear(); + fcBiasData.clear(); + return fcData; +} \ No newline at end of file diff --git a/submissions/cifar10/src/server_encrypted_compute.cpp b/submissions/cifar10/src/server_encrypted_compute.cpp new file mode 100644 index 0000000..878b6aa --- /dev/null +++ b/submissions/cifar10/src/server_encrypted_compute.cpp @@ -0,0 +1,73 @@ +// Copyright 2025 Google LLC +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "encryption_utils.h" +#include "params.h" +#include "resnet20_fheon.h" +#include "utils.h" +#include + +using namespace lbcrypto; + +int main(int argc, char *argv[]) { + + if (argc < 2 || !std::isdigit(argv[1][0])) { + std::cout << "Usage: " << argv[0] << " instance-size \n"; + std::cout << " Instance-size: 0-SINGLE, 1-SMALL, 2-MEDIUM, 3-LARGE\n"; + return 0; + } + auto size = static_cast(std::stoi(argv[1])); + InstanceParams prms(size); + + CryptoContext cc = read_crypto_context(prms); + // read_eval_keys(prms, cc); + PublicKey pk = read_public_key(prms); + PrivateKey sk = read_secret_key(prms); + + std::cout << " [server] Loading keys" << std::endl; + + Ctext ctxt; + fs::create_directories(prms.ctxtdowndir()); + std::cout << " [server] run encrypted CIFAR10 inference" << std::endl; + + FHEONHEController fheonHEController(cc); + for (size_t i = 0; i < prms.getBatchSize(); ++i) { + auto input_ctxt_path = prms.ctxtupdir() / ("cipher_input_" + std::to_string(i) + ".bin"); + if (!Serial::DeserializeFromFile(input_ctxt_path, ctxt, SerType::BINARY)) { + throw std::runtime_error("Failed to get ciphertexts from " + + input_ctxt_path.string()); + } + + std::string pubkey_dir = prms.pubkeydir().string() + "/"; + std::string sk_path = (prms.seckeydir() / "sk.bin").string(); + + auto start = std::chrono::high_resolution_clock::now(); + auto ctxtResult = resnet20(fheonHEController, cc, ctxt, pubkey_dir); + // auto ctxtResult = resnet20(fheonHEController, cc, ctxt); + + ofstream outFile; + outFile.open("./../results/resnet20/fhepredictions.txt", ios_base::app); + fheonHEController.read_inferenced_label_with_key(sk, ctxtResult, 10, outFile); + cout << std::endl; + + auto end = std::chrono::high_resolution_clock::now(); + auto duration = std::chrono::duration_cast(end - start); + std::cout << " [server] Execution time for ciphertext " << i + << " : " << duration.count() << " seconds" << std::endl; + auto result_ctxt_path = prms.ctxtdowndir() / ("cipher_result_" + std::to_string(i) + ".bin"); + Serial::SerializeToFile(result_ctxt_path, ctxtResult, SerType::BINARY); + cout << std::endl << std::endl; + } + return 0; +} diff --git a/submissions/cifar10/src/server_preprocess_model.cpp b/submissions/cifar10/src/server_preprocess_model.cpp new file mode 100644 index 0000000..a5ad894 --- /dev/null +++ b/submissions/cifar10/src/server_preprocess_model.cpp @@ -0,0 +1,10 @@ +// Copyright (c) 2025 HomomorphicEncryption.org +// All rights reserved. +// +// This software is licensed under the terms of the Apache v2 License. +// See the LICENSE.md file for details. +//============================================================================ + +int main(){ + return 0; +} \ No newline at end of file diff --git a/submissions/cifar10/weights/resnet20/layer0_conv1_bias.csv b/submissions/cifar10/weights/resnet20/layer0_conv1_bias.csv new file mode 100755 index 0000000..615fa38 --- /dev/null +++ b/submissions/cifar10/weights/resnet20/layer0_conv1_bias.csv @@ -0,0 +1 @@ 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diff --git a/submissions/cifar10/weights/resnet20/layer1_block1_conv1_bias.csv b/submissions/cifar10/weights/resnet20/layer1_block1_conv1_bias.csv new file mode 100755 index 0000000..9979b3c --- /dev/null +++ b/submissions/cifar10/weights/resnet20/layer1_block1_conv1_bias.csv @@ -0,0 +1 @@ +0.1974842,1.5404555,0.31470442,0.0,-0.041501492,1.2523941,0.9027004,0.88109803,1.2047718,0.032238614,0.9101026,-0.35913622,1.171645,1.0640604,1.0062197,0.015268445 diff --git a/submissions/cifar10/weights/resnet20/layer1_block1_conv1_weight.csv b/submissions/cifar10/weights/resnet20/layer1_block1_conv1_weight.csv new file mode 100755 index 0000000..56a7fea --- /dev/null +++ b/submissions/cifar10/weights/resnet20/layer1_block1_conv1_weight.csv @@ -0,0 +1 @@ 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diff --git a/submissions/cifar10/weights/resnet20/layer1_block1_conv2_bias.csv b/submissions/cifar10/weights/resnet20/layer1_block1_conv2_bias.csv new file mode 100755 index 0000000..0c7aa2b --- /dev/null +++ b/submissions/cifar10/weights/resnet20/layer1_block1_conv2_bias.csv @@ -0,0 +1 @@ +0.94978654,-0.3883415,-0.14621459,-0.76044416,0.13536829,0.14626873,-1.0131972,0.23386642,-0.9916983,0.79625297,0.3537549,0.69123787,-0.88844055,-0.31726363,0.10318578,0.0925816 diff --git a/submissions/cifar10/weights/resnet20/layer1_block1_conv2_weight.csv b/submissions/cifar10/weights/resnet20/layer1_block1_conv2_weight.csv new file mode 100755 index 0000000..eb38032 --- /dev/null +++ b/submissions/cifar10/weights/resnet20/layer1_block1_conv2_weight.csv @@ -0,0 +1 @@ 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diff --git a/submissions/cifar10/weights/resnet20/layer3_block1_conv1_bias.csv b/submissions/cifar10/weights/resnet20/layer3_block1_conv1_bias.csv new file mode 100755 index 0000000..2e99c3f --- /dev/null +++ b/submissions/cifar10/weights/resnet20/layer3_block1_conv1_bias.csv @@ -0,0 +1 @@ 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diff --git a/submissions/cifar10/weights/resnet20/layer3_block1_shortcut_bias.csv b/submissions/cifar10/weights/resnet20/layer3_block1_shortcut_bias.csv new file mode 100755 index 0000000..5878577 --- /dev/null +++ b/submissions/cifar10/weights/resnet20/layer3_block1_shortcut_bias.csv @@ -0,0 +1 @@ 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diff --git a/submissions/cifar10/weights/resnet20/layer_fc_bias.csv b/submissions/cifar10/weights/resnet20/layer_fc_bias.csv new file mode 100755 index 0000000..c1885db --- /dev/null +++ b/submissions/cifar10/weights/resnet20/layer_fc_bias.csv @@ -0,0 +1 @@ +0.31431013,-0.16034259,0.1805637,-0.016419556,0.22447334,-0.23526931,-0.015319572,-0.15109073,-0.01981494,-0.20407483 diff --git a/submissions/cifar10/weights/resnet20/layer_fc_weight.csv b/submissions/cifar10/weights/resnet20/layer_fc_weight.csv new file mode 100755 index 0000000..b429f1c --- /dev/null +++ b/submissions/cifar10/weights/resnet20/layer_fc_weight.csv @@ -0,0 +1 @@ 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