Differential Privacy and Risk Metrics for Creating Safe Data
Differential Privacy and Risk Metrics for Creating Safe Data
7 PagesThe whitepaper explains how differential privacy and risk metrics enable safe sharing of sensitive health data while preserving accuracy. Differential privacy protects individuals by ensuring their data does not significantly influence outputs, using randomness to create uncertainty and limit what can be inferred. Risk metrics complement this by measuring re-identification risk and guiding how much transformation, such as noise, is required. Benchmarks like minimum group sizes help determine appropriate privacy levels and ensure individuals cannot be singled out. The approach balances data utility and privacy by preserving statistical validity while reducing disclosure risks. Combined with secure environments and ongoing assessment, these methods support responsible data reuse and innovati
