Inferring Social Ties in Pervasive Networks: An On-Campus Comparative Study

Igor Bilogrevic (1) , Kévin Huguenin (1) , Murtuza Jadliwala (2) , Florent Lopez (3, 4, 5) , Jean-Pierre Hubaux (1) , Philip Ginzboorg (6, 7) , Valtteri Niemi (6, 8)

1 LCA - Laboratory for Computer Communications and Applications
2 EECS - Department of Electrical Engineering and Computer Science
3 ENSEEIHT - Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et de Télécommunications
4 EPFL - Ecole Polytechnique Fédérale de Lausanne
5 IRIT - Institut de recherche en informatique de Toulouse
6 NOKIA Research Center
7 Department of Information and Computer Science
8 Department of Mathematics

WiFi base stations are increasingly deployed in both public spaces and private companies, and the increase in their density poses a significant threat to the privacy of users. Prior studies have shown that it is possible to infer the social ties between users from their (co-)location traces but they lack one important component: the comparison of the inference accuracy between an internal attacker (e.g., a curious application running on the device) and a realistic external eavesdropper (e.g., a network of snifing stations) in the same field trial. We experimentally show that such an eavesdropper can infer the type of social ties between mobile users better than an internal attacker.

Domaines: Informatique ubiquitaire; Informatique mobile; Réseaux et télécommunications [cs.NI]

Igor Bilogrevic, Kévin Huguenin, Murtuza Jadliwala, Florent Lopez, Jean-Pierre Hubaux, et al.. Inferring Social Ties in Pervasive Networks: An On-Campus Comparative Study. 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), Sep 2013, Zurich, Switzerland. pp.1-4. ⟨hal-00835156⟩