Redrawing the boundaries on purchasing data from privacy-sensitive individuals

  • Authors:

  • Kobbi Nissim;Salil Vadhan;David Xiao

  • Affiliations:

  • Ben-Gurion University, Be'er Sheva, Israel & Harvard University, Cambridge, MA, USA;Harvard University, Cambridge, MA, USA;LIAFA and Université Paris 7, Paris, France

  • Venue:
  • Proceedings of the 5th conference on Innovations in theoretical computer science
  • Year:
  • 2014

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Abstract

We prove new positive and negative results concerning the existence of truthful and individually rational mechanisms for purchasing private data from individuals with unbounded and sensitive privacy preferences. We strengthen the impossibility results of Ghosh and Roth (EC 2011) by extending it to a much wider class of privacy valuations. In particular, these include privacy valuations that are based on (ε δ)-differentially private mechanisms for non-zero δ, ones where the privacy costs are measured in a per-database manner (rather than taking the worst case), and ones that do not depend on the payments made to players (which might not be observable to an adversary). To bypass this impossibility result, we study a natural special setting where individuals have monotonic privacy valuations, which captures common contexts where certain values for private data are expected to lead to higher valuations for privacy (e. g. having a particular disease). We give new mechanisms that are individually rational for all players with monotonic privacy valuations, truthful for all players whose privacy valuations are not too large, and accurate if there are not too many players with too-large privacy valuations. We also prove matching lower bounds showing that in some respects our mechanism cannot be improved significantly.