User Identity De-anonymization Based on Attributes

Online social networks provide platforms for people to interact with each other and share moments of their daily life. The online social network data are valuable for both academic and business studies, and are usually processed by anonymization methods before being published to third parties. However, several existing de-anonymization techniques can re-identify the users in anonymized networks. In light of this, we explore the impact of user attributes in social network de-anonymization in this paper. More specifically, we first quantify the significance of attributes in a social network, based on which we propose an attribute-based similarity measure; then we design an algorithm by exploiting attribute-based similarity to de-anonymize social network data; finally we employ a real-world dataset collected from Sina Weibo to conduct experiments, which demonstrate that our design can significantly improve the de-anonymization accuracy compared with a well-known baseline algorithm.