一种社交网络中隐私保护DCIGN算法研究

Research on DCIGN Algorithm for Privacy Protection in Social Network

提出了DCIGN算法,该算法采取以中心度量方式划分社区,依据社区的关联相似性,通过增加和删除边的方式对社交网络匿名化,使社区不可被区分,这样不仅可以更好抵挡来自于图结构方面的攻击,并大幅提升了整个社交网络的数据可用性。通过仿真实验证明,该算法在数据损失、时间复杂度及匿名质量等方面都有所提升。

In order to solve the privacy leakage problem of users in the process of data publishing,this paper proposes the DCIGN algorithm.The algorithm divides the community by means of central measurement,and anonymizes the social network by adding and deleting edges according to the association similarity of the community,so that the community can not be distinguished.This can not only better resist attacks from the graph structure,but also greatly improve the data availability of the entire social network.The simulation results show that the algorithm has improved in data loss,time complexity and anonymous quality.