
Have your data and hide it too: an introduction to differential privacy
12/22/2023
This post introduces differential privacy (DP) as a crucial enhancement to private aggregation techniques like DAP. It explains the limitations of DAP alone by demonstrating how aggregates can still reveal sensitive individual information through reconstruction attacks. DP is presented as a statistical framework that adds noise to aggregates, controlled by an epsilon parameter, to provide a rigorous mathematical guarantee of privacy. The post highlights the benefits of DP, including its generalizability across complex statistics, harmonization of security parameters, and graceful degradation of guarantees, contrasting it with ad-hoc privacy protections. It also mentions Cloudflare's role in standardizing DAP and developing an open-source implementation, with an intern tasked with devising a strategy to make DAP differentially private.