基于k-频繁子图聚类的二分图匿名方法

针对以二分图形式发布的社会网络隐私泄露问题，提出了一种面向敏感边识别攻击的社会网络二分图匿名方法。在已有k-安全分组的理论基础上，结合二分图的稀疏性和敏感边识别攻击形式，分别提出了正单向、逆单向以及完全（c1，c2）-安全性原则，并在此基础上，形式化地定义了一类抗敏感边识别攻击的社会网络二分图安全匿名问题；同时，还提出了一种基于k-频繁子图聚类的二分图划分算法和一种基于二分图（c1，c2）-安全性的匿名算法来保证发布二分图的安全性。实验结果表明，该算法在与已有方法相当时间开销的前提下，能产生更小的信息损失度，有效地抵制了敏感边识别攻击，实现了二分图的安全发布。

Novel bipartite graph anonymous method based on k-frequent subgraph clustering

In this paper, a novel bipartite graph anonymous method is proposed to against sensitive edges identification attacks from malicious users and to preserve the privacy of?members in social networks. A positive one-way （c1, c2）-security, a negative one-way （c1, c2）-security, and a two-way （c1, c2）-security principles are introduced. These definitions are based on the k-security group theory, the sparsity of bipartite graphs, and the sensitive edges identification attacks in social networks. A bipartite graph anonymous problem is defined to against sensitive edges identification attacks. A bigraph partitioning algorithm is presented on the basis of k-frequent subgraphs clustering and a bipartite graph anonymous algorithm is given to assure the safety of the published bipartite graph. The experimental results show that under the equal time-cost conditions, the proposed method not only produces less information loss than that of the existing methods, but also effectively resists sensitive edges identification attacks and realizes security release of bipartite graphs.