On information-theoretic privacy with general distortion cost functions

Kousha Kalantari, Lalitha Sankar, Oliver Kosut

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

7 Scopus citations

Abstract

The privacy-utility tradeoff problem is formulated as determining the privacy mechanism (random mapping) that minimizes the mutual information (a metric for privacy leakage) between the private features of the original dataset and a released version. The minimization is subject to a constraint on the average distortion cost defined as a function f evaluated for every distortion d between the public features and the released version of dataset. The asymptotic optimal leakage is derived both for general and stationary memoryless privacy mechanisms. It is shown that for convex cost functions there is no asymptotic loss in using stationary memoryless mechanisms. Of independent interest are the proof techniques developed here for arbitrary cost functions.

Original languageEnglish (US)
Title of host publication2017 IEEE International Symposium on Information Theory, ISIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2865-2869
Number of pages5
ISBN (Electronic)9781509040964
DOIs
StatePublished - Aug 9 2017
Event2017 IEEE International Symposium on Information Theory, ISIT 2017 - Aachen, Germany
Duration: Jun 25 2017Jun 30 2017

Other

Other2017 IEEE International Symposium on Information Theory, ISIT 2017
CountryGermany
CityAachen
Period6/25/176/30/17

Keywords

  • Distortion cost function
  • Mutual information leakage
  • Privacy utility tradeoff

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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  • Cite this

    Kalantari, K., Sankar, L., & Kosut, O. (2017). On information-theoretic privacy with general distortion cost functions. In 2017 IEEE International Symposium on Information Theory, ISIT 2017 (pp. 2865-2869). [8007053] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2017.8007053