Mining Social Interactions in Connection Traces of a Campus Wi-Fi Network
Identificadores
URI: https://hdl.handle.net/10481/81201Metadatos
Mostrar el registro completo del ítemAutor
Mañas-Martínez, Eduardo; Cabrera, Elena; Wasielewska, Katarzyna; Kotz, David; Camacho Páez, JoséEditorial
ACM SIGCOMM'21
Fecha
2021Patrocinador
This work was supported by Dartmouth College, by ACM SIGMOBILE, and (earlier) by US NSF award number 0454062.We appreciate the support of colleagues in the collection and anonymization of Wi-Fi data: most notably Tristan Henderson, Chris McDonald, and Nathan Schneider. This work was also supported by the Agencia Estatal de Investigación in Spain, grant No PID2020-113462RB-I00, and the European Union’s Horizon 2020 Marie Skłodowska-Curie grant agreement No 893146.Resumen
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.