Differentially private partition selection

Author:

Desfontaines Damien1,Voss James2,Gipson Bryant2,Mandayam Chinmoy2

Affiliation:

1. Tumult Labs

2. Google

Abstract

Abstract Many data analysis operations can be expressed as a GROUP BY query on an unbounded set of partitions, followed by a per-partition aggregation. To make such a query differentially private, adding noise to each aggregation is not enough: we also need to make sure that the set of partitions released is also differentially private. This problem is not new, and it was recently formally introduced as differentially private set union [14]. In this work, we continue this area of study, and focus on the common setting where each user is associated with a single partition. In this setting, we propose a simple, optimal differentially private mechanism that maximizes the number of released partitions. We discuss implementation considerations, as well as the possible extension of this approach to the setting where each user contributes to a fixed, small number of partitions.

Publisher

Walter de Gruyter GmbH

Subject

General Medicine

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Privately Answering Queries on Skewed Data via Per-Record Differential Privacy;Proceedings of the VLDB Endowment;2024-07

2. Bounded and Unbiased Composite Differential Privacy;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

3. TraVaS: Differentially Private Trace Variant Selection for Process Mining;Lecture Notes in Business Information Processing;2023

4. Distributed, Private, Sparse Histograms in the Two-Server Model;Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security;2022-11-07

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