# Geo-Indistinguishability: Differential Privacy for Location-Based Systems

Source: [Geo-Indistinguishability: Differential Privacy for Location-Based Systems](<https://www.sota2.com/research/paper/68fa614b1e209131ce9f37ec>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:56:35.880Z

Authors: Miguel E. Andr\\'es, Nicol\\'as E. Bordenabe, Konstantinos Chatzikokolakis, Catuscia Palamidessi

Published: 2012-12-10T07:09:21.000Z

## 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 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [10-NN on Beta synthetic (test)](<https://www.sota2.com/research/sota/10-nn-on-beta-synthetic-test.md>): 10-NN; Beta synthetic (test); Recall: 59.4.

- [10-NN on Gowalla (test)](<https://www.sota2.com/research/sota/10-nn-on-gowalla-test.md>): 10-NN; Gowalla (test); Recall: 44.6.

- [10-NN on Brightkite (test)](<https://www.sota2.com/research/sota/10-nn-on-brightkite-test.md>): 10-NN; Brightkite (test); Recall: 42.3.

- [10-NN on Gaussian synthetic (test)](<https://www.sota2.com/research/sota/10-nn-on-gaussian-synthetic-test.md>): 10-NN; Gaussian synthetic (test); Recall: 62.9.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1212.1984>)

[PDF](<https://arxiv.org/pdf/1212.1984.pdf>)