Buying private data without verification

Arpita Ghosh, Katrina Ligett, Aaron Roth, Grant Schoenebeck

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

43 Scopus citations

Abstract

We consider the problem of designing a survey to aggregate non-verifiable information from a privacy-sensitive population: an analyst wants to compute some aggregate statistic from the private bits held by each member of a population, but cannot verify the correctness of the bits reported by participants in his survey. Individuals in the population are strategic agents with a cost for privacy, ie, they not only account for the payments they expect to receive from the mechanism, but also their privacy costs from any information revealed about them by the mechanism's outcome - the computed statistic as well as the payments - to determine their utilities. How can the analyst design payments to obtain an accurate estimate of the population statistic when individuals strategically decide both whether to participate and whether to truthfully report their sensitive information' We design a differentially private peer-prediction mechanism [Miller et al. 2005] that supports accurate estimation of the population statistic as a Bayes-Nash equilibrium in settings where agents have explicit preferences for privacy. The mechanism requires knowledge of the marginal prior distribution on bits bi, but does not need full knowledge of the marginal distribution on the costs ci, instead requiring only an approximate upper bound. Our mechanism guarantees ε-differential privacy to each agent i against any adversary who can observe the statistical estimate output by the mechanism, as well as the payments made to the n-1 other agents j ≠; i. Finally, we show that with slightly more structured assumptions on the privacy cost functions of each agent [Chen et al. 2013], the cost of running the survey goes to 0 as the number of agents diverges.

Original languageAmerican English
Title of host publicationEC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation
PublisherAssociation for Computing Machinery
Pages931-948
Number of pages18
ISBN (Print)9781450325653
DOIs
StatePublished - 2014
Externally publishedYes
Event15th ACM Conference on Economics and Computation, EC 2014 - Palo Alto, CA, United States
Duration: 8 Jun 201412 Jun 2014

Publication series

NameEC 2014 - Proceedings of the 15th ACM Conference on Economics and Computation

Conference

Conference15th ACM Conference on Economics and Computation, EC 2014
Country/TerritoryUnited States
CityPalo Alto, CA
Period8/06/1412/06/14

Keywords

  • differential privacy
  • mechanism design
  • peer prediction

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