Local Pan-privacy for Federated Analytics
Vitaly Feldman, Audra McMillan, Guy N. Rothblum, Kunal Talwar
Abstract
Pan-privacy was proposed by Dwork et al. [2010] as an approach to designing a private analytics system that retains its privacy properties in the face of intrusions that expose the system's internal state. Motivated by federated telemetry applications, we study local panprivacy, where privacy should be retained under repeated unannounced intrusions on the local state. We consider the problem of monitoring the count of an event in a federated system, where event occurrences on a local device should be hidden even from an intruder on that device. We show that under reasonable constraints, the goal of providing information-theoretic differential privacy under intrusion is incompatible with collecting telemetry information. We then show that this problem can be solved in a scalable way using standard cryptographic primitives.
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Builds on3
- Practical Secure Aggregation for Privacy-Preserving Machine LearningKallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone et al.CCS 2017 · 3,936 citations
- The Discrete Gaussian for Differential PrivacyClément L. Canonne, Gautam Kamath, Thomas SteinkeNeurIPS 2020 · 355 citations
- The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure AggregationPeter Kairouz, Ziyu Liu, Thomas SteinkeICML 2021 · 291 citations
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