A server-side CKKS GPU library fully interoperable with OpenFHE.
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Updated
Jun 3, 2026 - Cuda
A server-side CKKS GPU library fully interoperable with OpenFHE.
A Skill for Anthropic Claude that enables Claude users to more easily create secure computation applications that use fully homomorphic encryption. The initial commit demonstrates an 11% improvement over non-skill Claude in satisfying application goals, at a cost increase of roughly 50% in tokens on the test suite.
Open-source FHE compiler and toolchain. Build fully homomorphic encryption applications with the nb DSL, instrumented OpenFHE, or a CUDA-style library API, record one Polynomial IR trace, and deploy to the Niobium accelerator
Harness and example implementation the FHE fetch-by-similarity workload
Beginner-friendly FHE learning examples for the Niobium FHE accelerator. Requires a Niobium SDK install (provided to licensed customers, e.g. on FOG terminals). Apache 2.0 source; proprietary runtime.
FHE Oracle — adversarial precision testing for Fully Homomorphic Encryption. Open-source edition (AGPL-3.0).
Drop-in encrypted Fairlearn metrics over CKKS. Same API surface; ciphertext arithmetic via TenSEAL or OpenFHE.
Reference implementation of the BHDR regression kernel: BSGS-hoisted diagonal Kernel SHAP regression under CKKS FHE.
Unified attack-replay regression harness for FHE libraries (SEAL, OpenFHE, Lattigo, tfhe-rs).
Docker base image with pre-built OpenFHE libraries for creating language bindings and applications. Ready-to-use development environment for homomorphic encryption projects.
Proxy simulation for evaluating encrypted LLM accuracy without running full CKKS inference. IIT Big Data X REU 2025, eScience 2025.
Application of Homomorphic Encryption for Financial Services
Encrypted Mamba LM inference under CKKS (FIDESlib/OpenFHE). Certified polynomial Mamba-2 surrogate (WikiText-2 ΔPPL +0.12%), verified decode lowering, GPU kernel in progress. New trunk: fhemamba/; the fhe_native_mamba3 package is a read-only archive.
A Benchmarking Framework for Fully Homomorphic Encryption Libraries
Privacy-preserving neural networks in C++ using OpenFHE. Analyzes performance impact (runtime/memory) on encrypted training and inference.
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