serval:BIB_770B8AF71C30
Inferring social ties in academic networks using short-range wireless communications
10.1145/2517840.2517842
Bilogrevic
I.
author
Huguenin
K.
author
Jadliwala
M.
author
Lopez
F.
author
Hubaux
J.-P.
author
Ginzboorg
P.
author
Niemi
V.
author
inproceedings
2013
ACM
Berlin, Germany
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society (WPES)
978-1-4503-2485-4
conference publication
179-188
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 connected users. Prior studies have provided evidence that it is possible to infer the social ties of users from their location and 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 a mobile device) and a realistic external eavesdropper in the same field trial. In this paper, we experimentally show that such an eavesdropper is able to infer the type of social relationships between mobile users better than an internal attacker. Moreover, our results indicate that by exploiting the underlying social community structure of mobile users, the accuracy of the inference attacks doubles. Based on our findings, we propose countermeasures to help users protect their privacy against eavesdroppers.
eng
60_published
peer-reviewed
University of Lausanne
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