Fast De-anonymization of Social Networks with Structural Information

• Main Authors: Yingxia Shao, Jialin Liu, Shuyang Shi, Yuemei Zhang, Bin Cui
• Format: Article
• Language: English
• Published: SpringerOpen 2019-03-01
• Series: Data Science and Engineering
• Subjects: Social network; De-anonymization; Privacy risk; Node similarity
• Online Access: http://link.springer.com/article/10.1007/s41019-019-0086-8

Abstract Ever since the social networks became the focus of a great number of researches, the privacy risks of published network data have also raised considerable concerns. To evaluate users’ privacy risks, researchers have developed methods to de-anonymize the networks and identify the same person in the different networks. However, the existing solutions either require high-quality seed mappings for cold start, or exhibit low accuracy without fully exploiting the structural information, and entail high computation expense. In this paper, we propose a fast and effective seedless network de-anonymization approach simply relying on structural information, named RoleMatch. RoleMatch equips with a new pairwise node similarity measure and an efficient node matching algorithm. Through testing RoleMatch with both real and synthesized social networks, which are anonymized by several popular anonymization algorithms, we demonstrate that the RoleMatch receives superior performance compared with existing de-anonymization algorithms.