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

WU Hongwei1，2, ZHANG Jianpei1, YANG Jing1

1. 1.College of Computer Science and Technology, Harbin Engineering University, Harbin 150000, China
2.Computer Centre, Harbin University of Science and Technology, Harbin 150080, China
• Online:2013-09-01 Published:2013-09-13

### 基于k-频繁子图聚类的二分图匿名方法

1. 1.哈尔滨工程大学 计算机科学与技术学院，哈尔滨 150000
2.哈尔滨理工大学 计算中心，哈尔滨 150080

Abstract: 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.