On the relation between identifiability, differential privacy, and mutual-information privacy

Weina Wang, Lei Ying, Junshan Zhang

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

12 Scopus citations

Abstract

This paper investigates the relation between three different notions of privacy: identifiability, differential privacy and mutual-information privacy. Under a privacy-distortion framework, where the distortion is defined to be the expected Hamming distance between the input and output databases, we establish some fundamental connections between these three privacy notions. Given a maximum distortion D, let ε i(D) denote the smallest (best) identifiability level, and ε d(D) the smallest differential privacy level. Then we characterize ε i(D) and ε d(D), and prove that ε i(D) - εx ≤ ε d(D) ≤ εε i(D) for D in some range, where εx is a constant depending on the distribution of the original database X, and diminishes to zero when the distribution of X is uniform. Furthermore, we show that identifiability and mutual-information privacy are consistent in the sense that given a maximum distortion D in some range, there is a mechanism that optimizes the identifiability level and also achieves the best mutual-information privacy.

Original languageEnglish (US)
Title of host publication2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1086-1092
Number of pages7
ISBN (Print)9781479980093
DOIs
StatePublished - Jan 30 2015
Event2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014 - Monticello, United States
Duration: Sep 30 2014Oct 3 2014

Other

Other2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
CountryUnited States
CityMonticello
Period9/30/1410/3/14

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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