Privacy Auctions for Recommender Systems

  • Pranav Dandekar
  • Nadia Fawaz
  • Stratis Ioannidis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7695)


We study a market for private data in which a data analyst publicly releases a statistic over a database of private information. Individuals that own the data incur a cost for their loss of privacy proportional to the differential privacy guarantee given by the analyst at the time of the release. The analyst incentivizes individuals by compensating them, giving rise to a privacy auction. Motivated by recommender systems, the statistic we consider is a linear predictor function with publicly known weights. The statistic can be viewed as a prediction of the unknown data of a new individual, based on the data of individuals in the database. We formalize the trade-off between privacy and accuracy in this setting, and show that a simple class of estimates achieves an order-optimal trade-off. It thus suffices to focus on auction mechanisms that output such estimates. We use this observation to design a truthful, individually rational, proportional-purchase mechanism under a fixed budget constraint. We show that our mechanism is 5-approximate in terms of accuracy compared to the optimal mechanism, and that no truthful mechanism can achieve a 2 − ε approximation, for any ε > 0.


Recommender System Private Data Linear Predictor Data Analyst Privacy Cost 
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

  • Pranav Dandekar
    • 1
  • Nadia Fawaz
    • 2
  • Stratis Ioannidis
    • 2
  1. 1.Stanford UniversityUSA
  2. 2.TechnicolorUSA

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