GeoPal: Friend spam detection in social networks using private location proofs Conference

cited authors

  • Carbunar, B; Rahman, M; Azimpourkivi, M; Davis, D


  • Friend spam, adversarial invitations sent to social network users, exposes victims to a suite of privacy, spear phishing and malware vulnerabilities. In this paper, we use the location history of users to detect friend spam. We posit that the user trust in friends is associated with their co-location frequency. We exploit this hypothesis to introduce GeoPal, a framework that carefully accesses the potentially sensitive location history of users to privately prove their past location claims, and to privately compute and update fuzzy co-location affinities with other users. We build GeoPal on PLP, a protocol we develop to privately collect proofs of user past locations. We confirm our hypothesis through a user study with 68 participants: 57% and 70% of the friends never met in person are not remembered and are not talked to, respectively, by the participants. In contrast, 86% of the friends met daily or weekly are either family, close or regular friends. We highlight the relevance of friend spam: 75% of the participants have at least one friend whom they do not recall. We show that GeoPal is practical: a Nexus 5 can process more thank 20K location proofs per second.

publication date

  • November 2, 2016

Digital Object Identifier (DOI)