Optimizing fitness-for-use of differentially private linear Queries

Yingtai Xiao, Zeyu Ding, Yuxin Wang, Danfeng Zhang, Daniel Kifer

Research output: Contribution to journalConference articlepeer-review

Abstract

In practice, differentially private data releases are designed to support a variety of applications. A data release is fit for use if it meets target accuracy requirements for each application. In this paper, we consider the problem of answering linear queries under differential privacy subject to per-query accuracy constraints. Existing practical frameworks like the matrix mechanism do not provide such fine-grained control (they optimize total error, which allows some query answers to be more accurate than necessary, at the expense of other queries that become no longer useful). Thus, we design a fitness-for-use strategy that adds privacy-preserving Gaussian noise to query answers. The covariance structure of the noise is optimized to meet the fine-grained accuracy requirements while minimizing the cost to privacy.

Original languageEnglish (US)
Pages (from-to)1730-1742
Number of pages13
JournalProceedings of the VLDB Endowment
Volume14
Issue number10
DOIs
StatePublished - 2021
Event47th International Conference on Very Large Data Bases, VLDB 2021 - Virtual, Online
Duration: Aug 16 2021Aug 20 2021

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)

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