Nicolás E. Bordenabe

arXiv2014

Optimal Geo-Indistinguishable Mechanisms for Location Privacy

We consider the geo-indistinguishability approach to location privacy, and the trade-off with respect to utility. We show that, given a desired degree of geo-indistinguishability, it is possible to construct a mechanism that minimizes the service quality loss, using linear programming techniques. In addition we show that, under certain conditions, such mechanism also provides optimal privacy in the sense of Shokri et al. Furthermore, we propose a method to reduce the number of constraints of the linear program from cubic to quadratic, maintaining the privacy guarantees and without affecting significantly the utility of the generated mechanism. This reduces considerably the time required to solve the linear program, thus enlarging significantly the location sets for which the optimal mechanisms can be computed.

cs.CR↗

arXiv2014

Geo-Indistinguishability: Differential Privacy for Location-Based Systems

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 study geo-indistinguishability, 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. Our 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 perturbation technique for achieving geo-indistinguishability by adding controlled random noise to the user's location. We demonstrate the applicability of our technique on a LBS application. Finally, we compare our mechanism with other ones in the literature. It turns our that our mechanism offers the best privacy guarantees, for the same utility, among all those which do not depend on the prior.

cs.CR↗