UPDATE

CONTRIBUTING.md

@@ -10,11 +10,11 @@ NIST is accepting the following contributions.
 **Feedback:** Help the community. Provide feedback on tools and use cases.
 
 ## Contribution Focus Areas
-Please limit your contributions to the topics of de-identification or privacy risk assessment. We welcome [feedback](mailto:collabspace@nist.gov) on future topics of interest.
+Please limit your contributions to the topics of disassociability or privacy risk assessment. We welcome [feedback](mailto:collabspace@nist.gov) on future topics of interest.
 
-**De-identification:** a technique or process applied to a dataset with the goal of preventing or limiting certain types of privacy risks to individuals, protected groups, and establishments, while still allowing for the production of aggregate statistics. This focus area includes a broad scope of de-identification to allow for noise-introducing techniques such as differential privacy, data masking, and the creation of synthetic datasets that are based on privacy-preserving models.
+**Disassociability:** This focus area can help system designers and engineers consider how to enable the processing of personal information or events without association to individuals or devices beyond the operational requirements of the system. These tools and use cases also support the achievement of the Dissociated Processing Subcategory (CT.DP.P) of the NIST Privacy Framework. 
 
-**Privacy Risk Assessment:** a process that helps organizations to analyze and assess privacy risks for individuals arising from the processing of their data. This focus area includes, but is not limited to, risk models, risk assessment methodologies, and approaches to determining privacy risk factors.
+**Privacy Risk Assessment:** A process that helps organizations to analyze and assess privacy risks for individuals arising from the processing of their data. This focus area includes, but is not limited to, risk models, risk assessment methodologies, and approaches to determining privacy risk factors.
 
 # How to Contribute
 
@@ -28,7 +28,7 @@ Tools and use cases are contributed via pull requests, while feedback is contrib
 
 3. In your branch: 
 
-	A. Create a new directory within the relevant tool or use case directory: tools/de-identification, tools/risk-assessment, use-cases/de-identification, or use-cases/risk-assessment. Example: *tools/de-identification/[your-contribution-name]*
+	A. Create a new directory within the relevant tool or use case directory: tools/disassociability, tools/risk-assessment, use-cases/disassociability, or use-cases/risk-assessment. Example: *tools/disassociability/[your-contribution-name]*
 
 	B. Name the directory to describe your contribution. 
 

README.md

@@ -2,9 +2,9 @@
 The NIST Privacy Engineering Collaboration Space is an online venue open to the public where practitioners can discover, share, discuss, and improve upon open source tools, solutions, and processes that support privacy engineering and risk management.
 
 ## Focus Areas
-We have launched this space with an initial focus on de-identification and privacy risk assessment tools and use cases, and welcome [feedback](mailto:collabspace@nist.gov) on topics of interest from the community. 
+We have launched this space with an initial focus on disassociability and privacy risk assessment tools and use cases, and welcome [feedback](mailto:collabspace@nist.gov) on topics of interest from the community. 
 
-* **De-identification:** a technique or process applied to a dataset with the goal of preventing or limiting certain types of privacy risks to individuals, protected groups, and establishments, while still allowing for the production of aggregate statistics. This focus area includes a broad scope of de-identification to allow for noise-introducing techniques such as differential privacy, data masking, and the creation of synthetic datasets that are based on privacy-preserving models.
+* **Disassociability:** a technique or process applied to a dataset with the goal of preventing or limiting certain types of privacy risks to individuals, protected groups, and establishments, while still allowing for the production of aggregate statistics. This focus area includes a broad scope of disassociability to allow for noise-introducing techniques such as differential privacy, data masking, and the creation of synthetic datasets that are based on privacy-preserving models.
 
 * **Privacy Risk Assessment:** a process that helps organizations to analyze and assess privacy risks for individuals arising from the processing of their data. This focus area includes, but is not limited to, risk models, risk assessment methodologies, and approaches to determining privacy risk factors. 
 
@@ -26,7 +26,7 @@ Tools and use cases are contributed via pull requests, while feedback is contrib
 
 3. In your branch: 
 
-	A. Create a new directory within the relevant tool or use case directory: tools/de-identification, tools/risk-assessment, use-cases/de-identification, or use-cases/risk-assessment. Example: *tools/de-identification/[your-contribution-name]*
+	A. Create a new directory within the relevant tool or use case directory: tools/disassociability, tools/risk-assessment, use-cases/disassociability, or use-cases/risk-assessment. Example: *tools/disassociability/[your-contribution-name]*
 
 	B. Name the directory to describe your contribution. 
 
@@ -52,7 +52,7 @@ Submit an [issue](https://github.com/usnistgov/PrivacyEngCollabSpace/issues/new)
 
 ## Browse Tools and Use Cases
 
-Interested in tools or use cases for de-identification and privacy risk assessment? **Browse the contributions [here](https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/browse).**
+Interested in tools or use cases for disassociability and privacy risk assessment? **Browse the contributions [here](https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering/collaboration-space/browse).**
 
 ## Operating Rules 
 
@@ -78,7 +78,7 @@ This platform is provided as a public service. Information, data, and software p
 
 ## Moderators 
 
-### De-Identification Moderators
+### Disassociability Moderators
 
 ![Joseph Near](https://github.com/usnistgov/PrivacyEngCollabSpace/blob/master/assets/joseph-near.jpg)
 
@@ -88,11 +88,15 @@ This platform is provided as a public service. Information, data, and software p
 
 **David Darais [@davdar]:** David Darais is a Principal Scientist at Galois, Inc. and supports NIST as a moderator for the Privacy Engineering Collaboration Space. David's research focuses on tools for achieving reliable software in critical, security-sensitive, and privacy-sensitive systems. David received his B.S. from the University of Utah, M.S. from Harvard University and Ph.D. from the University of Maryland.
 
-### Privacy Risk Management Moderator
+### Privacy Risk Management Moderators
 
-![Katie Boeckl](https://github.com/usnistgov/PrivacyEngCollabSpace/blob/master/assets/katie-boeckl.jpg)
+![Nakia Grayson](https://github.com/usnistgov/PrivacyEngCollabSpace/blob/master/assets/nakia-grayson.jpg)
 
-**Katie Boeckl [@kboeckl]:** Katie Boeckl is a privacy risk strategist at NIST. As part of the Privacy Engineering Program, Katie develops privacy risk management guidance, collaborates on the development of international privacy standards, and works to advance tools for privacy engineering and risk management. Katie has a B.A. in English from the University of Maryland, College Park, where she specialized in technology through a digital cultures honors program.
+**Nakia Grayson [@ngrayson1]:** Nakia Grayson is an IT Security Specialist with the Privacy Engineering Program at the National Institute of Standards and Technology (NIST). She supports the Privacy Engineering Program with development of privacy risk management best practices, guidance and communications efforts. She also leads Supply Chain Assurance project efforts at the National Cybersecurity Center of Excellence (NCCoE). Nakia serves as the Contracting Officer Representative for NIST cybersecurity contracts. She holds a Bachelor’s in Criminal Justice from University of Maryland-Eastern Shore and a Master’s in Information Technology, Information Assurance and Business Administration from the University of Maryland University College.
+
+![Meghan Anderson](https://github.com/usnistgov/PrivacyEngCollabSpace/blob/master/assets/meghan-anderson.jpg)
+
+**Meghan Anderson [@manderson11]:** Meghan Anderson is a Privacy Risk Strategist with the Privacy Engineering Program at the National Institute of Standards and Technology, U.S. Department of Commerce. She supports the development of privacy engineering, international privacy standards, and privacy risk management guidance. Meghan has a Bachelor’s in Emergency Preparedness, Homeland Security, and Cybersecurity with a concentration in Cybersecurity and a minor in Economics from the University of Albany, SUNY and a Master’s in Cybersecurity from the Georgia Institute of Technology (Georgia Tech).
 
 ## NIST Privacy Engineering Program
 Learn about NIST's Privacy Engineering Program by visiting our [website](https://www.nist.gov/itl/applied-cybersecurity/privacy-engineering).

templates/tool-template.md

@@ -2,9 +2,9 @@
 
 **Name of Tool:**
 
-**Primary Focus Area (select one):** De-identification or Privacy Risk Assessment
+**Primary Focus Area (select one):** Disassociability or Privacy Risk Assessment
 
-**De-identification Keywords (select any relevant):** Differential Privacy, K-Anonymity, Anonymization, Access Control, Information Leakage, Algorithmic Fairness, Verification of Algorithms, Machine Learning, Database Queries, Synthetic Data Generation, Location Data, Adversarial Examples, Other/Propose New Keyword
+**Disassociability Keywords (select any relevant):** Differential Privacy, K-Anonymity, Anonymization, Access Control, Information Leakage, Algorithmic Fairness, Verification of Algorithms, Machine Learning, Database Queries, Synthetic Data Generation, Location Data, Adversarial Examples, Other/Propose New Keyword
 
 **Brief Description:**
 

templates/use-case-template.md

@@ -2,9 +2,9 @@
 
 **Name of Use Case:**
 
-**Primary Focus Area (select one):** De-identification or Privacy Risk Assessment
+**Primary Focus Area (select one):** Disassociability or Privacy Risk Assessment
 
-**De-identification Keywords (select any relevant):** Differential Privacy, K-Anonymity, Anonymization, Access Control, Information Leakage, Algorithmic Fairness, Verification of Algorithms, Machine Learning, Database Queries, Synthetic Data Generation, Location Data, Other/Propose New Keyword
+**Disassociability Keywords (select any relevant):** Differential Privacy, K-Anonymity, Anonymization, Access Control, Information Leakage, Algorithmic Fairness, Verification of Algorithms, Machine Learning, Database Queries, Synthetic Data Generation, Location Data, Other/Propose New Keyword
 
 **Brief Description:**
 

tools/README.md

@@ -1,9 +1,9 @@
 # Focus Areas
-Tools and use cases are currently focused on de-identification and privacy risk assessment. We welcome [feedback](mailto:collabspace@nist.gov) on future topics of interest.
+Tools and use cases are currently focused on disassociability and privacy risk assessment. We welcome [feedback](mailto:collabspace@nist.gov) on future topics of interest.
 
-**De-identification:** a technique or process applied to a dataset with the goal of preventing or limiting certain types of privacy risks to individuals, protected groups, and establishments, while still allowing for the production of aggregate statistics. This focus area includes a broad scope of de-identification to allow for noise-introducing techniques such as differential privacy, data masking, and the creation of synthetic datasets that are based on privacy-preserving models.
+**Disassociability:** This focus area can help system designers and engineers consider how to enable the processing of personal information or events without association to individuals or devices beyond the operational requirements of the system. These tools and use cases also support the achievement of the Dissociated Processing Subcategory (CT.DP.P) of the NIST Privacy Framework. 
 
-**Privacy Risk Assessment:** a process that helps organizations to analyze and assess privacy risks for individuals arising from the processing of their data. This focus area includes, but is not limited to, risk models, risk assessment methodologies, and approaches to determining privacy risk factors.
+**Privacy Risk Assessment:** A process that helps organizations to analyze and assess privacy risks for individuals arising from the processing of their data. This focus area includes, but is not limited to, risk models, risk assessment methodologies, and approaches to determining privacy risk factors.
 
 # Contribute Feedback 
 

tools/de-identification/HyFL-Framework/README.md

@@ -0,0 +1,13 @@
+# HyFL Framework for anomaly detection in financial transactions.
+
+- **Name of Tool:** HyFL framework for financial anomaly detection
+- **Primary Focus Area:** De-identification
+- **Privacy Risk Assessment Keywords:** Differential Privacy, Information Leakage, Anomaly Detection, Federated Learning, Encryption
+- **Brief Desription:** The repository provides a framework HyFL as a tool to detect anomaly in financial transactions. This framework supporst a hybrid federated learning paradigm to offer secure and privacy-aware learning and inference for financial anomaly detection.
+- **GitHub User Serving as POC:** hbzhang879@gmail.com
+- **Affiliation/Organazations Contributing:**
+  - Illidan Lab, Michigan State University, USA
+  - DENOS Lab, University of Calgary, Canada
+
+# For a Linked Tool
+**Tool Link:** https://github.com/illidanlab/HyFL

tools/de-identification/MusCAT/README.md

@@ -0,0 +1,18 @@
+# MusCAT
+
+**Primary Focus Area (select one):** De-identification
+
+**De-identification Keywords (select any relevant):** Differential Privacy, Multiparty Homomorphic Encryption, Machine Learning, Federated Learning 
+
+**Brief Description:**
+MusCAT is a multi-scale, hybrid federated system for privacy-preserving epidemic surveillance and risk prediction. 
+It combines differential privacy, multiparty homomorphic encryption, and federated learning to jointly analyze private data held by multiple federation units with formal privacy guarantees.
+This software implements Team MusCAT's solution to the [U.S. PETs Prize Challenge](https://www.drivendata.org/competitions/group/nist-federated-learning/) (Pandemic Forecasting).
+Team MusCAT won [first place](https://drivendata.co/blog/federated-learning-pets-prize-winners-phase1) for the white paper (Phase 1) and [second place](https://drivendata.co/blog/federated-learning-pets-prize-winners-phases-2-3) in the final stage (Phase 2) of the Challenge.
+
+
+**GitHub User Serving as POC (or Email Address):** @hhcho
+
+**Affiliation/Organization(s) Contributing (if relevant):** Broad Institute, MIT, Harvard Business School, UT Austin, University of Toronto
+
+**Tool Link:** https://github.com/hhcho/muscat