Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters
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Abstract
In this paper, we study the concurrent composition of interactive mechanisms with adaptively chosen privacy-loss parameters. In this setting, the adversary can interleave queries to existing interactive mechanisms, as well as create new ones. We prove that every valid privacy filter and odometer for noninteractive mechanisms extends to the concurrent composition of interactive mechanisms if privacy loss is measured using $(\epsilon, \delta)$-DP, $f$-DP, or R\'enyi DP of fixed order. Our results offer strong theoretical foundations for enabling full adaptivity in composing differentially private interactive mechanisms, showing that concurrency does not affect the privacy guarantees. We also provide an implementation for users to deploy in practice.
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Funding data
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Division of Behavioral and Cognitive Sciences
Grant numbers BCS-2218803 -
Alfred P. Sloan Foundation
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Apple
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Simons Foundation
Grant numbers Simons Investigator Award -
Computing Research Association
Grant numbers Computing Innovation Fellowship -
Boston University
Grant numbers Census grant