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Conference Papers Year : 2014

Quantifying the Effect of Co-Location Information on Location Privacy


In addition to revealing, while on the go, their locations to online services, mobile users increasingly report their co-locations with other users. For instance, they tag the names of the friends they are with, in the messages and in the pictures they post on social networking websites. Combined with (possibly obfuscated) location information, such co-locations can be used to improve the inference of the users' locations, thus further threatening their location privacy: as co-location information is taken into account, not only a user's reported locations and mobility patterns can be used to localize her, but also those of her friends (and the friends of their friends and so on). In this paper, we study this problem by quantifying the effect of co-location information on location privacy, with respect to an adversary such as a social network operator that has access to such information. We formalize the problem and derive an optimal inference algorithm that incorporates such co-location information, yet at the cost of high complexity. We propose two polynomial-time approximate inference algorithms and we extensively evaluate their performance on a real dataset. Our experimental results show that, even in the case where the adversary considers co-locations with only a single friend of the targeted user, the median location privacy of the user is decreased by up to 75% in a typical setting. Even in the case where a user does not disclose any location information, her privacy can decrease by up to 37% due to the information reported by other users.
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hal-00978629 , version 1 (28-05-2014)



Alexandra-Mihaela Olteanu, Kévin Huguenin, Reza Shokri, Jean-Pierre Hubaux. Quantifying the Effect of Co-Location Information on Location Privacy. 14th Privacy Enhancing Technologies Symposium (PETS), Jul 2014, Amsterdam, Netherlands. pp.184-203, ⟨10.1007/978-3-319-08506-7_10⟩. ⟨hal-00978629⟩
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