Towards inferring communication patterns in online social networks
Balsa, Ero (Katholieke Universiteit Leuven)
Pérez-Solà, Cristina (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
Diaz, Claudia (Katholieke Universiteit Leuven)

Data:	2017
Resum:	The separation between the public and private spheres on online social networks is known to be, at best, blurred. On the one hand, previous studies have shown how it is possible to infer private attributes from publicly available data. On the other hand, no distinction exists between public and private data when we consider the ability of the online social network (OSN) provider to access them. Even when OSN users go to great lengths to protect their privacy, such as by using encryption or communication obfuscation, correlations between data may render these solutions useless. In this article, we study the relationship between private communication patterns and publicly available OSN data. Such a relationship informs both privacy-invasive inferences as well as OSN communication modelling, the latter being key toward developing effective obfuscation tools. We propose an inference model based on Bayesian analysis and evaluate, using a real social network dataset, how archetypal social graph features can lead to inferences about private communication. Our results indicate that both friendship graph and public traffic data may not be informative enough to enable these inferences, with time analysis having a non-negligible impact on their precision.
Ajuts:	European Commission 653497 European Commission 644371 Ministerio de Economía y Competitividad TIN2014-55243-P Ministerio de Economía y Competitividad FPU-AP2010-0078 Agència de Gestió d'Ajuts Universitaris i de Recerca 2014/SGR-691
Nota:	Grup de recerca: Security of Networks and Distributed Applications (SENDA)
Drets:	Tots els drets reservats.
Llengua:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Matèria:	Online social networks ; Communication ; Inference ; Privacy
Publicat a:	ACM Transactions on internet technology, Vol. 17, issue 3 (Jul. 2017) , art. 32, ISSN 1533-5399

DOI: 10.1145/3093897

Post-print 29 p, 1.8 MB

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