关键词: 社会网络, 敏感链接, 链接披露, 相似度量

Abstract: Many applications of social networks require link anonymity due to the sensitive nature of relationship, while link inference used by attackers in social networks could lead to link disclosure. To disclose the sensitive link relationship between nodes, attackers always depend on graph structure features to carry out their attacking objectives. The previous work only focuses on single structure feature as the background knowledge as well as overlooks the structural proximity of nodes, which will result in inferior inference. Therefore, this paper proposes an efficient inference framework based on node proximity in large-scale social networks. This framework includes matrix decomposition based on graph-clustering, computing proximity of nodes by singular value decomposition for every cluster. The paper also proposes an efficient algorithm, called LinkIn, which uses node proximity as the inferring condition of Bayes’ theorem to boost the posterior probability of links. Experimental results show that the framework outperforms the single methods, and is efficient and scalable in boosting the accuracy of link inference.

Key words: social network, sensitive link, link disclosure, proximity measure