Privacy Amplification via Shuffling: Unified, Simplified, and Tightened
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened
The shuffle model of differential privacy provides promising privacy-utility balances in decentralized, privacy-preserving data analysis. However, the current analyses of privacy amplification via shuffling lack both tightness and generality. To address this issue, we propose the variation-ratio reduction as a comprehensive framework for privacy amplification in both single-message and multi-message …