Attribute-Enhanced De-anonymization of Online Social Networks

Online Social Networks (OSNs) have transformed the way that people socialize. However, when OSNs bring people convenience, privacy leakages become a growing worldwide problem. Although several anonymization approaches are proposed to protect information of user identities and social relationships, existing de-anonymization techniques have proved that users in the anonymized network can be re-identified by using an external reference social network collected from the same network or other networks with overlapping users. In this paper, we propose a novel social network de-anonymization mechanism to explore the impact of user attributes on the accuracy of de-anonymization. More specifically, we propose an approach to quantify diversities of user attribute values and select valuable attributes to generate the multipartite graph. Next, we partition this graph into communities, and then map users on the community level and the network level respectively. Finally, we employ a real-world dataset collected from Sina Weibo to evaluate our approach, which demonstrates that our mechanism can achieve a better de-anonymization accuracy compared with the most influential de-anonymization method.