POSTER: Protecting Location Privacy on Road Networks

Takagi, Shun; Cao, Yang; Asano, Yasuhito; Yoshikawa, Masatoshi

doi:10.1145/3320269.3405442

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Additionally, we propose the graph-exponential mechanism (GEM), which satisfies GeoGI. These proposals appeared in the preliminary version [38], [41] of this paper.…”

Section: Introductionmentioning

confidence: 99%

Geo-Graph-Indistinguishability: Location Privacy on Road Networks with Differential Privacy

Takagi

Cao

Asano

et al. 2023

IEICE Trans. Inf. & Syst.

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In recent years, concerns about location privacy are increasing with the spread of location-based services (LBSs). Many methods to protect location privacy have been proposed in the past decades. Especially, perturbation methods based on Geo-Indistinguishability (GeoI), which randomly perturb a true location to a pseudolocation, are getting attention due to its strong privacy guarantee inherited from differential privacy. However, GeoI is based on the Euclidean plane even though many LBSs are based on road networks (e.g. ride-sharing services). This causes unnecessary noise and thus an insufficient tradeoff between utility and privacy for LBSs on road networks. To address this issue, we propose a new privacy notion, Geo-Graph-Indistinguishability (GeoGI), for locations on a road network to achieve a better tradeoff. We propose Graph-Exponential Mechanism (GEM), which satisfies GeoGI. Moreover, we formalize the optimization problem to find the optimal GEM in terms of the tradeoff. However, the computational complexity of a naive method to find the optimal solution is prohibitive, so we propose a greedy algorithm to find an approximate solution in an acceptable amount of time. Finally, our experiments show that our proposed mechanism outperforms GeoI mechanisms, including optimal GeoI mechanism, with respect to the tradeoff.

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“…Additionally, we propose the graph-exponential mechanism (GEM), which satisfies GeoGI. These proposals appeared in the preliminary version [38], [41] of this paper.…”

Section: Introductionmentioning

confidence: 99%