Geometry of privacy and utility

Bing Rong Lin, Daniel Kifer

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

4 Scopus citations

Abstract

One of the important challenges in statistical privacy is the design of algorithms that maximize a utility measure subject to restrictions imposed by privacy considerations. In this paper we examine large classes of privacy definitions and utility measures. We identify their geometric characteristics and some common properties of optimal privacy-preserving algorithms.

Original languageEnglish (US)
Title of host publication2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages281-284
Number of pages4
DOIs
StatePublished - Dec 1 2013
Event2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Austin, TX, United States
Duration: Dec 3 2013Dec 5 2013

Publication series

Name2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

Other

Other2013 1st IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013
CountryUnited States
CityAustin, TX
Period12/3/1312/5/13

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Signal Processing

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  • Cite this

    Lin, B. R., & Kifer, D. (2013). Geometry of privacy and utility. In 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings (pp. 281-284). [6736870] (2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings). https://doi.org/10.1109/GlobalSIP.2013.6736870