Differentially private partition selection
Authors: Damien Desfontaines (Tumult Labs), James Voss (Google), Bryant Gipson (Google), Chinmoy Mandayam (Google)
Volume: 2022
Issue: 1
Pages: 339–352
DOI: https://doi.org/10.2478/popets-2022-0017
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.
Keywords: data privacy, differential privacy
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