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Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters

12 September 2023
Samuel Haney
Michael Shoemate
Grace Tian
Salil P. Vadhan
Andrew Vyrros
Vicki Xu
Wanrong Zhang
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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 (ϵ,δ)(\epsilon, \delta)(ϵ,δ)-DP, fff-DP, or R\ényi 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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