See all of the de-identification and privacy risk assessment contributions to the Privacy Engineering Collaboration Space.
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.
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.
Tools and use cases are currently focused on de-identification and privacy risk assessment. We welcome feedback on future topics of interest.