Privacy under hard distortion constraints

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

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

11 Scopus citations


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
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


Conference2018 IEEE Information Theory Workshop, ITW 2018


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

ASJC Scopus subject areas

  • Information Systems

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