Privacy Preservation in Social Network against Public Neighborhood Attacks

Abstract

Recently, privacy preservation in publishing social network has become one of the most notable challenges. Researchers have developed a variety of methods to protect individual's privacy while maintaining utility at the same time. There exists such a situation that an adversary may identify the privacy of a victim with the background knowledge of public neighborhoods. Unfortunately, most of previous researches only focus on unclassified neighborhood attacks and ignore that public neighborhoods whose information is completely open will bring greater risk than private neighborhoods. In view of the availability of users' information, we adopt k-anonymity which belongs to graph modification methods to protect private users from public neighborhood attacks. Although k-anonymity could defend users from re-identification, a group of nodes may share the same sensitive labels which may be exploited by attackers to speculate private information. So we adopt l-diversity to guard the sensitive labels. We conduct our experiments in some social networks, and the results show that our method is effective.