On the Reliability of Profile Matching Across Large Online Social Networks

Abstract : Matching the profiles of a user across multiple online social networks brings opportunities for new services and applications as well as new insights on user online behavior, yet it raises serious privacy concerns. Prior literature has showed that it is possible to accurately match profiles, but their evaluation focused only on sampled datasets. In this paper, we study the extent to which we can reliably match profiles in practice, across real-world social networks , by exploiting public attributes, i.e., information users publicly provide about themselves. Today's social networks have hundreds of millions of users, which brings completely new challenges as a reliable matching scheme must identify the correct matching profile out of the millions of possible profiles. We first define a set of properties for profile attributes–Availability, Consistency , non-Impersonability, and Discriminability (ACID)–that are both necessary and sufficient to determine the reliability of a matching scheme. Using these properties, we propose a method to evaluate the accuracy of matching schemes in real practical cases. Our results show that the accuracy in practice is significantly lower than the one reported in prior literature. When considering entire social networks, there is a non-negligible number of profiles that belong to different users but have similar attributes, which leads to many false matches. Our paper sheds light on the limits of matching profiles in the real world and illustrates the correct methodology to evaluate matching schemes in realistic scenarios.
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Communication dans un congrès
KDD'15: ACM SIGDD Conference on Knowledge Discovery and Data Mining, Aug 2015, Sydeny, Australia. ACM SIGDD Conference on Knowledge Discovery and Data Mining, 〈10.1145/2783258.2788601〉
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Contributeur : Renata Teixeira <>
Soumis le : mercredi 10 juin 2015 - 14:33:21
Dernière modification le : mardi 17 avril 2018 - 11:23:56

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Oana Goga, Patrick Loiseau, Robin Sommer, Renata Teixeira, Krishna P. Gummadi. On the Reliability of Profile Matching Across Large Online Social Networks. KDD'15: ACM SIGDD Conference on Knowledge Discovery and Data Mining, Aug 2015, Sydeny, Australia. ACM SIGDD Conference on Knowledge Discovery and Data Mining, 〈10.1145/2783258.2788601〉. 〈hal-01162402〉

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