Maximum likelihood postprocessing for differential privacy under consistency constraints

Jaewoo Lee, Yue Wang, Daniel Kifer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

When analyzing data that has been perturbed for privacy reasons, one is often concerned about its usefulness. Recent research on differential privacy has shown that the accuracy of many data queries can be improved by post-processing the perturbed data to ensure consistency constraints that are known to hold for the original data. Most prior work converted this post-processing step into a least squares minimization problem with customized efficient solutions. While improving accuracy, this approach ignored the noise distribution in the perturbed data. In this paper, to further improve accuracy, we formulate this post-processing step as a constrained maximum likelihood estimation problem, which is equivalent to constrained L1 minimization. Instead of relying on slow linear program solvers, we present a faster generic recipe (based on ADMM) that is suitable for a wide variety of applications including differentially private contingency tables, histograms, and the matrix mechanism (linear queries). An added benefit of our formulation is that it can often take direct advantage of algorithmic tricks used by the prior work on least-squares post-processing. An extensive set of experiments on various datasets demonstrates that this approach significantly improve accuracy over prior work.

Original languageEnglish (US)
Title of host publicationKDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages635-644
Number of pages10
ISBN (Electronic)9781450336642
DOIs
StatePublished - Aug 10 2015
Event21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015 - Sydney, Australia
Duration: Aug 10 2015Aug 13 2015

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Volume2015-August

Other

Other21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2015
CountryAustralia
CitySydney
Period8/10/158/13/15

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems

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    Lee, J., Wang, Y., & Kifer, D. (2015). Maximum likelihood postprocessing for differential privacy under consistency constraints. In KDD 2015 - Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 635-644). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Vol. 2015-August). Association for Computing Machinery. https://doi.org/10.1145/2783258.2783366