Privacy under hard distortion constraints

Jiachun Liao, Oliver Kosut, Lalitha Sankar, Flavio P. Calmon

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

Abstract

We study the problem of data disclosure with privacy guarantees, wherein the utility of the disclosed data is ensured via a hard distortion constraint. Unlike average distortion, hard distortion provides a deterministic guarantee of fidelity. For the privacy measure, we use a tunable information leakage measure, namely maximal α-leakage (α ∈ [1, ∞]), and formulate the privacy-utility tradeoff problem. The resulting solution highlights that under a hard distortion constraint, the nature of the solution remains unchanged for both local and non-local privacy requirements. More precisely, we show that both the optimal mechanism and the optimal tradeoff are invariant for any α > 1; i.e., the tunable leakage measure only behaves as either of the two extrema, i.e., mutual information for α = 1 and maximal leakage for α = ∞.

Original languageEnglish (US)
Title of host publication2018 IEEE Information Theory Workshop, ITW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538635995
DOIs
StatePublished - Jan 15 2019
Event2018 IEEE Information Theory Workshop, ITW 2018 - Guangzhou, China
Duration: Nov 25 2018Nov 29 2018

Publication series

Name2018 IEEE Information Theory Workshop, ITW 2018

Conference

Conference2018 IEEE Information Theory Workshop, ITW 2018
CountryChina
CityGuangzhou
Period11/25/1811/29/18

Keywords

  • F-divergence
  • Hard distortion
  • Maximal α-leakage
  • Privacy-utility tradeoff

ASJC Scopus subject areas

  • Information Systems

Cite this

Liao, J., Kosut, O., Sankar, L., & Calmon, F. P. (2019). Privacy under hard distortion constraints. In 2018 IEEE Information Theory Workshop, ITW 2018 [8613385] (2018 IEEE Information Theory Workshop, ITW 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITW.2018.8613385