Hiding Information in Social Networks from De-anonymization Attacks by Using Identity Separation

Social networks allow their users to mark their profile attributes, relationships as private in order to guarantee privacy, although private information get occasionally published within sanitized datasets offered to third parties, such as business partners. Today, powerful de-anonymization attacks exist that enable the finding of corresponding nodes within these datasets and public network data (e. g., crawls of other networks) solely by considering structural information. In this paper, we propose an identity management technique, namely identity separation, as a tool for hiding information from attacks aiming to achieve large-scale re-identification. By simulation experiments we compare the protective strength of identity management to the state-of-the-art attack. We show that while a large fraction of participating users are required to repel the attack, with the proper settings it is possible to effectively hide information, even for a handful of users. In addition, we propose a user-controllable method based on decoy nodes, which turn out to be successful for information hiding as at most 3.33% of hidden nodes are revealed in our experiments.