NIST's 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.
NIST has launched this space with an initial focus on de-identification and privacy risk assessment. Please limit your contributions to these topics. NIST welcomes feedback 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.
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.
Contributing to the Space
Contributions are made through our GitHub repository, and they come in three categories:
- Tool: A tool can be an open source solution or process, ranging from software to frameworks.
- Use Case: A use case is an example of an organization processing data about individuals for some explicit purpose(s) (e.g., where a goal is to prevent re-identification of the data during its processing, improve privacy risk assessment practices).
- Feedback: Help the community. Provide feedback on tools and use cases.
Tools and use cases are contributed via pull requests, while feedback is contributed via issues.
Need help or have feedback?
Email us for assistance with using the space, or to submit feedback on future topics of interest.