Take It or Leave It: Running a Survey When Privacy Comes at a Cost

  • Katrina Ligett
  • Aaron Roth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7695)


In this paper, we consider the problem of estimating a potentially sensitive (individually stigmatizing) statistic on a population. In our model, individuals are concerned about their privacy, and experience some cost as a function of their privacy loss. Nevertheless, they would be willing to participate in the survey if they were compensated for their privacy cost. These cost functions are not publicly known, however, nor do we make Bayesian assumptions about their form or distribution. Individuals are rational and will misreport their costs for privacy if doing so is in their best interest. Ghosh and Roth recently showed in this setting, when costs for privacy loss may be correlated with private types, if individuals value differential privacy, no individually rational direct revelation mechanism can compute any non-trivial estimate of the population statistic. In this paper, we circumvent this impossibility result by proposing a modified notion of how individuals experience cost as a function of their privacy loss, and by giving a mechanism which does not operate by direct revelation. Instead, our mechanism has the ability to randomly approach individuals from a population and offer them a take-it-or-leave-it offer. This is intended to model the abilities of a surveyor who may stand on a street corner and approach passers-by.


Private Data Laplace Distribution Privacy Cost Participation Decision Differential Privacy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Katrina Ligett
    • 1
  • Aaron Roth
    • 2
  1. 1.Computing and Mathematical Sciences and Division of the Humanities and Social SciencesCaltechUSA
  2. 2.Department of Computer and Information SciencesUniversity of PennsylvaniaUSA

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