MVG mechanism: Differential privacy under matrix-valued query

Thee Chanyaswad, Alex Dytso, H. Vincent Poor, Prateek Mittal

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

5 Scopus citations

Abstract

Differential privacy mechanism design has traditionally been tailored for a scalar-valued query function. Although many mechanisms such as the Laplace and Gaussian mechanisms can be extended to a matrix-valued query function by adding i.i.d. noise to each element of the matrix, this method is often suboptimal as it forfeits an opportunity to exploit the structural characteristics typically associated with matrix analysis. To address this challenge, we propose a novel differential privacy mechanism called the Matrix-Variate Gaussian (MVG) mechanism, which adds a matrix-valued noise drawn from a matrix-variate Gaussian distribution, and we rigorously prove that the MVG mechanism preserves (?, d)-differential privacy. Furthermore, we introduce the concept of directional noise made possible by the design of the MVG mechanism. Directional noise allows the impact of the noise on the utility of the matrix-valued query function to be moderated. Finally, we experimentally demonstrate the performance of our mechanism using three matrix-valued queries on three privacy-sensitive datasets. We find that the MVG mechanism can notably outperforms four previous state-of-the-art approaches, and provides comparable utility to the non-private baseline.

Original languageEnglish (US)
Title of host publicationCCS 2018 - Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages230-246
Number of pages17
ISBN (Electronic)9781450356930
DOIs
StatePublished - Oct 15 2018
Event25th ACM Conference on Computer and Communications Security, CCS 2018 - Toronto, Canada
Duration: Oct 15 2018 → …

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Other

Other25th ACM Conference on Computer and Communications Security, CCS 2018
CountryCanada
CityToronto
Period10/15/18 → …

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

Keywords

  • Differential privacy
  • Directional noise
  • MVG mechanism
  • Matrix-valued query
  • Matrix-variate Gaussian

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