Differential Privacy

A mathematical framework for adding calibrated noise to datasets or model outputs to protect individual privacy while preserving statistical utility. Provides formal, provable privacy guarantees unlike anonymisation techniques that can often be reversed. Used by organisations including Apple, Google, and the US Census Bureau.