Community-Based Identity Validation on Online Social Networks

Abstract

Identity management in online social networks (OSNs) is a challenging, yet important requirement for effective privacy protection and trust management. Literature offers several proposals addressing issues related to identity breaches and/or identity related attacks on OSNs, but only a few aim at giving means to judge users' reliability in terms of trustworthiness of their claimed identities. In this paper, we propose an identity validation process that relies on OSN community feedback to assign to OSN users identity trustworthiness levels. For this purpose, we define a community based supervised learning process to detect the set of attributes in a user profile for which it is expected to see a correlation among their values (e.g., job and salary). Once these correlated attribute sets are identified, the profile of a target user is judged by a selected group of raters to estimate her identity trustworthiness level. We demonstrate the effectiveness of our proposal through experimentation under two different scenarios and using real data. The experiments' results under the two scenarios demonstrate the effectiveness and meaningfulness of our proposal.