Privacy-preserving measurement and machine learning

This post details Cloudflare's participation in the IETF's Privacy Preserving Measurement working group and their implementation of the Distributed Aggregation Protocol (DAP) in their open-source aggregator server, Daphne. It explains the core principles of DAP, including data minimization and the use of secret sharing and multi-party computation to aggregate data without exposing individual measurements. The post also discusses challenges like input validation and non-linear aggregation, and how zero-knowledge proofs and Prio are used to address them.