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With the increasing popularity of hand-held devices, location-basedapplications and services have access to accurate and real-time locationinformation, raising serious privacy concerns for their users. The recentlyintroduced notion of geo-indistinguishability tries to address this problem byadapting the well-known concept of differential privacy to the area oflocation-based systems. Although geo-indistinguishability presents variousappealing aspects, it has the problem of treating space in a uniform way,imposing the addition of the same amount of noise everywhere on the map. Inthis paper we propose a novel elastic distinguishability metric that warps thegeometrical distance, capturing the different degrees of density of each area.As a consequence, the obtained mechanism adapts the level of noise whileachieving the same degree of privacy everywhere. We also show how such anelastic metric can easily incorporate the concept of a "geographic fence" thatis commonly employed to protect the highly recurrent locations of a user, suchas his home or work. We perform an extensive evaluation of our technique bybuilding an elastic metric for Paris' wide metropolitan area, using semanticinformation from the OpenStreetMap database. We compare the resulting mechanismagainst the Planar Laplace mechanism satisfying standardgeo-indistinguishability, using two real-world datasets from the Gowalla andBrightkite location-based social networks. The results show that the elasticmechanism adapts well to the semantics of each area, adjusting the noise as wemove outside the city center, hence offering better overall privacy.

Constructing elastic distinguishability metrics for location privacy