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

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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Publication Details

Journal
Journal of Privacy and Confidentiality
Published
2025-08-31
DOI
https://doi.org/10.29012/jpc.918
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Wanrong Zhang, Salil Vadhan, Michael Shoemate, Samuel Haney et al.
Journal of Privacy and Confidentiality
Privacy-Preserving Technologies in Data
article

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

Wanrong Zhang, Salil Vadhan, Michael Shoemate, Samuel Haney, Grace Tian, Andrew Vyrros, Vicki Xu
article en

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.

Journal of Privacy and ConfidentialityVol. 15(2)
Harvard University Press (US)
National Science Foundation, Alfred P. Sloan Foundation, Simons Foundation, Computing Research Association, Boston University, Apple, Division of Behavioral and Cognitive Sciences
Openalex Percentile: Top 88%
Privacy-Preserving Technologies in Data
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Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss Parameters — Wanrong Zhang, Salil Vadhan, et al. · Journal of Privacy and Confidentiality (2025) | TGRS Research Map | TGRS