Composition Theorems for Interactive Differential Privacy
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Abstract (arXiv)
An interactive mechanism is an algorithm that stores a data set and answers adaptively chosen queries to it. The mechanism is called differentially private, if any adversary cannot distinguish whether a specific individual is in the data set by interacting with the mechanism. We study composition properties of differential privacy in concurrent compositions. In this setting, an adversary interacts with k interactive mechanisms in parallel and can interleave its queries to the mechanisms arbitrarily. Previously, Vadhan and Wang [2021] proved an optimal concurrent composition theorem for pure-differential privacy. We significantly generalize and extend their results. Namely, we prove optimal parallel composition properties for several major notions of differential privacy in the literature, including approximate DP, R'enyi DP, and zero-concentrated DP. Our results demonstrate that the adversary gains no advantage by interleaving its queries to independently running mechanisms. Hence, interactivity is a feature that differential privacy grants us for free. Concurrently and independently of our work, Vadhan and Zhang [2022] proved an optimal concurrent composition theorem for f-DP [Dong et al., 2022], which implies our result for the approximate DP case.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jul 2022.
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Citing papers per year: 2022: 2; 2023: 4; 2024: 1; 2025: 3.
10 papers.
- 2022Composition of Differential Privacy & Privacy Amplification by Subsamplingcited by 42
- 2022~Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimizationcited by 16
- 2023Faster Differentially Private Convex Optimization via Second-Order Methodscited by 13
- 2025Privacy amplification by random allocationcited by 13
- 2023Concentrated Differential Privacy for Banditscited by 10
- 2023Personalized Graph Federated Learning with Differential Privacycited by 4
- 2025Private Learning of Littlestone Classes, Revisitedcited by 3
- 2023Adaptive Privacy Composition for Accuracy-first Mechanismscited by 2
- 2025Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settingscited by 1
- 2024A Quantization-based Technique for Privacy Preserving Distributed Learningcited by 0
- 2025Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings1 Oct 2025
- 2025Private Learning of Littlestone Classes, Revisited30 Sep 2025
- 2025Privacy amplification by random allocation12 Feb 2025
- 2024A Quantization-based Technique for Privacy Preserving Distributed Learning26 Jun 2024
- 2023Concentrated Differential Privacy for Bandits1 Sep 2023
- 2023Adaptive Privacy Composition for Accuracy-first Mechanisms24 Jun 2023
- 2023Personalized Graph Federated Learning with Differential Privacy10 Jun 2023
- 2023Faster Differentially Private Convex Optimization via Second-Order Methods22 May 2023
- 2022~Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization11 Nov 2022
- 2022Composition of Differential Privacy & Privacy Amplification by Subsampling2 Oct 2022