Answering Private Linear Queries Adaptively using the Common Mechanism

Yingtai Xiao, Guanhong Wang, Danfeng Zhang, Daniel Kifer

Research output: Contribution to journalConference articlepeer-review

Abstract

When analyzing confidential data through a privacy filter, a data scientist often needs to decide which queries will best support their intended analysis. For example, an analyst may wish to study noisy two-way marginals in a dataset produced by a mechanism M1. But, if the data are relatively sparse, the analyst may choose to examine noisy one-way marginals, produced by a mechanismM2, instead. Since the choice of whether to use M1 orM2 is data-dependent, a typical differentially private workflow is to first split the privacy loss budget ρ into two parts: ρ1 and ρ2, then use the first part ρ1 to determine which mechanism to use, and the remainder ρ2 to obtain noisy answers from the chosen mechanism. In a sense, the first step seems wasteful because it takes away part of the privacy loss budget that could have been used to make the query answers more accurate. In this paper, we consider the question of whether the choice between M1 and M2 can be performed without wasting any privacy loss budget. For linear queries, we propose a method for decomposing M1 and M2 into three parts: (1) a mechanism M that captures their shared information, (2) a mechanism M′1 that captures information that is specific to M1, (3) a mechanism M2 that captures information that is specific to M2. Running M andM1 together is completely equivalent to running M1 (both in terms of query answer accuracy and total privacy cost ρ). Similarly, running M and M2 together is completely equivalent to running M2. Since M will be used no matter what, the analyst can use its output to decide whether to subsequently run M1 (thus recreating the analysis supported by M1) or M2 (recreating the analysis supported by M2), without wasting privacy loss budget.

Original languageEnglish (US)
Pages (from-to)1883-1896
Number of pages14
JournalProceedings of the VLDB Endowment
Volume16
Issue number8
DOIs
StatePublished - 2023
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: Aug 28 2023Sep 1 2023

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science

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