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.
Contributions are made through our GitHub repository, and they come in three categories:
Tools and use cases are contributed via pull requests, while feedback is contributed via issues.
Email us for assistance with using the space, or to submit feedback on future topics of interest.