Composition Theorems for Interactive Differential Privacy
Xin Lyu
摘要
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, Rényi 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.
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引用它的顶会 Paper9
- Bounded and Unbiased Composite Differential PrivacyKai Zhang, Yanjun Zhang, Ruoxi Sun, Pei-Wei Tsai 等S&P 2024 · 被引用 54 次
- Faster Differentially Private Convex Optimization via Second-Order MethodsArun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha ThakurtaNeurIPS 2023 · 被引用 18 次
- Concurrent Composition Theorems for Differential PrivacySalil P. Vadhan, Wanrong ZhangSTOC 2023 · 被引用 11 次
- Adaptive Privacy Composition for Accuracy-first MechanismsRyan M. Rogers, Gennady Samorodnitsky, Zhiwei Steven Wu, Aaditya RamdasNeurIPS 2023 · 被引用 6 次
- Concurrent Composition for Interactive Differential Privacy with Adaptive Privacy-Loss ParametersSamuel Haney, Michael Shoemate, Grace Tian, Salil P. Vadhan 等CCS 2023 · 被引用 4 次
它引用的顶会 Paper3
- Fully-Adaptive Composition in Differential PrivacyJustin Whitehouse, Aaditya Ramdas, Ryan Rogers, Steven WuICML 2023 · 被引用 56 次
- Improving Sparse Vector Technique with Renyi Differential PrivacyYuqing Zhu, Yu-Xiang WangNeurIPS 2020 · 被引用 25 次
- Concurrent Composition Theorems for Differential PrivacySalil P. Vadhan, Wanrong ZhangSTOC 2023 · 被引用 11 次
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