Geo-indistinguishability: differential privacy for location-based systems

Miguel E. Andrés, Nicolás E. Bordenabe, Konstantinos Chatzikokolakis, Catuscia Palamidessi

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

879 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationCCS 2013
Subtitle of host publicationproceedings of the 20th ACM Conference on Computer and Communications Security
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Number of pages14
ISBN (Print)9781450324779
Publication statusPublished - 2013
Externally publishedYes
EventACM Conference on Computer and Communications Security (20th : 2013) - Berlin, Germany
Duration: 4 Nov 20138 Nov 2013


ConferenceACM Conference on Computer and Communications Security (20th : 2013)
CityBerlin, Germany


  • Location-based services
  • Location privacy
  • Location obfuscation
  • Differential privacy
  • Planar Laplace distribution


Dive into the research topics of 'Geo-indistinguishability: differential privacy for location-based systems'. Together they form a unique fingerprint.

Cite this