Inferring Social Ties in Academic Networks Using Short-Range Wireless Communications

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.


Presented at:
12th Workshop on Privacy in the Electronic Society (WPES 2013), co-located with ACM CCS, Berlin, Germany, November 4, 2013
Year:
2013
Laboratories:




 Record created 2013-09-11, last modified 2018-03-17

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