Interpretable DP
A suite of tools for statistics and machine learning with interpretable risk-based privacy guarantees for easy auditability and communication in regulated environments.
Technical Layer
Audience: Researchers, differential privacy experts
riskcal
Tools for computing f-DP trade-off curves for differentially private algorithms, and calibrating their noise scale to operational privacy risk measures (attack advantage, or attack TPR and FPR).
pip install riskcal
gdpnum
Correct numerical accounting in terms of Gaussian differential privacy for more interpretable and auditable guarantees in privacy-preserving machine learning.
pip install gdpnum
Application Layer
Audience: Researchers, data scientists
Coming soon: Synthetic data with interpretable privacy guarantees.
About
Publications
This software suite has emerged as a result of several independent scientific collaborations.
People
Funding
The effort is partially funded by the Swiss National Science Foundation (SNF):
Bridging Regulatory Data Protection Standards and Model Sharing in Healthcare