The value of privacy: Strategic data subjects, incentive mechanisms and fundamental limits

Weina Wang, Lei Ying, Junshan Zhang

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

25 Scopus citations

Abstract

We study the value of data privacy in a game-theoretic model of trading private data, where a data collector pur- chases private data from strategic data subjects (individu-als) through an incentive mechanism. The private data of each individual represents her knowledge about an underly- ing state, which is the information that the data collector desires to learn. Different from most of the existing work on privacy-aware surveys, our model does not assume the data collector to be trustworthy. Then, an individual takes full control of its own data privacy and reports only a privacy- preserving version of her data. In this paper, the value of ϵ units of privacy is measured by the minimum payment of all nonnegative payment mech- Anisms, under which an individual's best response at a Nash equilibrium is to report the data with a privacy level of ϵ. The higher ϵ is, the less private the reported data is. We derive lower and upper bounds on the value of privacy which are asymptotically tight as the number of data subjects be- comes large. Speciffically, the lower bound assures that it is impossible to use less amount of payment to buy ϵ units of privacy, and the upper bound is given by an achievable pay- ment mechanism that we designed. Based on these funda- mental limits, we further derive lower and upper bounds on the minimum total payment for the data collector to achieve a given learning accuracy target, and show that the total payment of the designed mechanism is at most one individ- ual's payment away from the minimum.

Original languageEnglish (US)
Title of host publicationSIGMETRICS/ Performance 2016 - Proceedings of the SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Science
PublisherAssociation for Computing Machinery, Inc
Pages249-260
Number of pages12
ISBN (Electronic)9781450342667
DOIs
StatePublished - Jun 14 2016
Event13th Joint International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS / IFIP Performance 2016 - Antibes Juan-les-Pins, France
Duration: Jun 14 2016Jun 18 2016

Other

Other13th Joint International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS / IFIP Performance 2016
CountryFrance
CityAntibes Juan-les-Pins
Period6/14/166/18/16

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computational Theory and Mathematics
  • Hardware and Architecture

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    Wang, W., Ying, L., & Zhang, J. (2016). The value of privacy: Strategic data subjects, incentive mechanisms and fundamental limits. In SIGMETRICS/ Performance 2016 - Proceedings of the SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Science (pp. 249-260). Association for Computing Machinery, Inc. https://doi.org/10.1145/2896377.2901461