Have your data and hide it too: an introduction to differential privacy

This post introduces differential privacy (DP) as a crucial enhancement to private aggregation protocols like DAP. It explains how DP adds noise to aggregates to prevent attackers from inferring individual data points, addressing the limitations of multi-party computation alone. The post highlights the risks of deanonymization attacks on census data and LLMs, and demonstrates how DP can protect against these by providing a rigorous mathematical framework for privacy guarantees, complementing existing privacy-preserving measurement efforts.