Quantifying Interdependent Privacy Risks with Location Data

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Ressource 1Télécharger: BIB_9154ADDB1A1A.P001.pdf (1854.63 [Ko])
Etat: Serval
Version: de l'auteur
ID Serval
serval:BIB_9154ADDB1A1A
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Quantifying Interdependent Privacy Risks with Location Data
Périodique
IEEE Transactions on Mobile Computing
Auteur(s)
Olteanu A.-M., Huguenin K., Shokri R., Humbert M., Hubaux J.-P.
ISSN
1536-1233
Statut éditorial
Publié
Date de publication
03/2017
Peer-reviewed
Oui
Volume
16
Numéro
3
Pages
829-842
Langue
anglais
Résumé
Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the names of the friends they are with. The users' IP addresses also constitute a source of co-location information. 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, considering 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 some approximate inference algorithms, including a solution that relies on the belief propagation algorithm executed on a general Bayesian network model, and we extensively evaluate their performance. Our experimental results show that, even in the case where the adversary considers co-locations of the targeted user with a single friend, the median location privacy of the user is decreased by up to 62% in a typical setting. We also study the effect of the different parameters (e.g., the settings of the location-privacy protection mechanisms) in different scenarios.
Mots-clé
Social networks, Location privacy, Co-location, Inference
Création de la notice
03/11/2016 12:39
Dernière modification de la notice
03/03/2018 19:28
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