Geo-indistinguishability: Differential Privacy for Location-based Systems. Andrés, M., E., Bordenabe, N., E., Chatzikokolakis, K., & Palamidessi, C. In Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, of CCS '13, pages 901-914, 2013. ACM.
Geo-indistinguishability: Differential Privacy for Location-based Systems [link]Website  abstract   bibtex   
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.
@inProceedings{
 title = {Geo-indistinguishability: Differential Privacy for Location-based Systems},
 type = {inProceedings},
 year = {2013},
 identifiers = {[object Object]},
 keywords = {differential-privacy,location-privacy},
 pages = {901-914},
 websites = {http://dx.doi.org/10.1145/2508859.2516735},
 publisher = {ACM},
 city = {New York, NY, USA},
 series = {CCS '13},
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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.},
 bibtype = {inProceedings},
 author = {Andrés, Miguel E and Bordenabe, Nicolás E and Chatzikokolakis, Konstantinos and Palamidessi, Catuscia},
 booktitle = {Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security}
}

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