Amplification by Shuffling without Shuffling
Borja Balle, James Bell, Adrià Gascón
摘要
Motivated by recent developments in the shuffle model of differential privacy, we propose a new approximate shuffling functionality called Alternating Shuffle, and provide a protocol implementing alternating shuffling in a single-server threat model where the adversary observes all communication. Unlike previous shuffling protocols in this threat model, the per-client communication of our protocol only grows sub-linearly in the number of clients. Moreover, we study the concrete efficiency of our protocol and show it can improve per-client communication by one or more orders of magnitude with respect to previous (approximate) shuffling protocols. We also show a differential privacy amplification result for alternating shuffling analogous to the one for uniform shuffling, and demonstrate that shuffling-based protocols for secure summation based a construction of Ishai et al. [35] remain secure under the Alternating Shuffle. In the process we also develop a protocol for exact shuffling in single-server threat model with amortized logarithmic communication per-client which might be of independent interest.
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- Practical Secure Aggregation for Privacy-Preserving Machine LearningKallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone 等CCS 2017 · 被引用 3,936 次
- Efficient Private Statistics with Succinct SketchesLuca Melis, George Danezis, Emiliano De CristofaroNDSS 2016 · 被引用 128 次
- Privacy Amplification via Random Check-InsBorja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar 等NeurIPS 2020 · 被引用 86 次
- Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by ShufflingVitaly Feldman, Audra McMillan, Kunal TalwarFOCS 2021 · 被引用 76 次
- Private Summation in the Multi-Message Shuffle ModelBorja Balle, James Bell, Adrià Gascón, Kobbi NissimCCS 2020 · 被引用 52 次
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