An Information-Theoretic Privacy Criterion for Query Forgery in Information Retrieval

  • David Rebollo-Monedero
  • Javier Parra-Arnau
  • Jordi Forné
Part of the Communications in Computer and Information Science book series (CCIS, volume 259)

Abstract

In previous work, we presented a novel information-theoretic privacy criterion for query forgery in the domain of information retrieval. Our criterion measured privacy risk as a divergence between the user’s and the population’s query distribution, and contemplated the entropy of the user’s distribution as a particular case. In this work, we make a twofold contribution. First, we thoroughly interpret and justify the privacy metric proposed in our previous work, elaborating on the intimate connection between the celebrated method of entropy maximization and the use of entropies and divergences as measures of privacy. Secondly, we attempt to bridge the gap between the privacy and the information-theoretic communities by substantially adapting some technicalities of our original work to reach a wider audience, not intimately familiar with information theory and the method of types.

Keywords

Information Retrieval Relative Entropy Trusted Third Party Reference Distribution Large Deviation Theory 
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 2011

Authors and Affiliations

  • David Rebollo-Monedero
    • 1
  • Javier Parra-Arnau
    • 1
  • Jordi Forné
    • 1
  1. 1.Department of Telematics EngineeringTechnical University of Catalonia (UPC)BarcelonaSpain

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