Mining social interactionsin connection traces of a campus wi-fi network

Abstract

Wi-Fi technologies have become one of the most popular means for Internet access. As a result, the use of mobile devices has become ubiquitous and instrumental for society. A device can be identified through its MAC address within an autonomous system. Although some devices attempt to anonymize MAC addresses via randomization, these techniques are not used once the device is associated to the network [7]. As a result, device identification poses a privacy problem in large-scale (e.g., campus-wide) Wi-Fi deployments [5]: if the mobile device can be located, the user who carries that device can also be located. In turn, location information leads to the possibility to extract private knowledge from Wi-Fi users, like social interactions, movement habits, and so forth. In this poster we report preliminary work in which we infer social interactions of individuals from Wi-Fi connection traces in the campus network at Dartmouth College [2]. We make the following contributions: (i) we propose several definitions of a pseudocorrelation matrix from Wi-Fi connection traces, which measure similarity between devices or users according to their temporal association profile to the Access Points (APs); (ii) we evaluate the accuracy of these pseudo-correlation variants in a simulation environment; and (iii) we contrast results with those found on a real trace.