Add functionality to the library that enables assessment of privacy risk in datasets. This should help data engineering teams evaluate how protected individual users are from re-identification based on available attributes. Consider implementing:
- Metrics such as k-anonymity, l-diversity, or t-closeness
- Scanning datasets to identify quasi-identifiers
- Reporting risk scores and guidance for further anonymisation
This feature will help improve the privacy assurance for datasets processed by the library.
Add functionality to the library that enables assessment of privacy risk in datasets. This should help data engineering teams evaluate how protected individual users are from re-identification based on available attributes. Consider implementing:
This feature will help improve the privacy assurance for datasets processed by the library.