Geo-indistinguishability: differential privacy for location-based systems

  • Authors:

  • Miguel E. Andrés;Nicolás E. Bordenabe;Konstantinos Chatzikokolakis;Catuscia Palamidessi

  • Affiliations:

  • Ecole Polytechnique, Paris, France;INRIA and Ecole Polytechnique, Paris, France;CNRS and Ecole Polytechnique, Paris, France;INRIA and Ecole Polytechnique, Paris, France

  • Venue:
  • Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
  • Year:
  • 2013

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Abstract

The growing popularity of location-based systems, allowing unknown/untrusted servers to easily collect huge amounts of information regarding users' location, has recently started raising serious privacy concerns. In this paper we introduce geoind, a formal notion of privacy for location-based systems that protects the user's exact location, while allowing approximate information -- typically needed to obtain a certain desired service -- to be released. This privacy definition formalizes the intuitive notion of protecting the user's location within a radius $r$ with a level of privacy that depends on r, and corresponds to a generalized version of the well-known concept of differential privacy. Furthermore, we present a mechanism for achieving geoind by adding controlled random noise to the user's location. We describe how to use our mechanism to enhance LBS applications with geo-indistinguishability guarantees without compromising the quality of the application results. Finally, we compare state-of-the-art mechanisms from the literature with ours. It turns out that, among all mechanisms independent of the prior, our mechanism offers the best privacy guarantees.