Local Pan-Privacy for Federated Analytics
March 14, 2025 Β· Declared Dead Β· π International Conference on Machine Learning
"No code URL or promise found in abstract"
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Authors
Vitaly Feldman, Audra McMillan, Guy N. Rothblum, Kunal Talwar
arXiv ID
2503.11850
Category
cs.CR: Cryptography & Security
Cross-listed
cs.DS,
cs.LG
Citations
0
Venue
International Conference on Machine Learning
Last Checked
4 months ago
Abstract
Pan-privacy was proposed by Dwork et al. 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 pan-privacy, 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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