基于k-同构和局部随机化的隐私保护方法

Privacy Preserving Method Based on k-isomorphism and Local Randomization

在社会网络数据被大量收集和发布的过程中存在隐私信息泄露的情况,社会网络隐私保护问题引起了人们的关注。本文针对单一方式的社会网络隐私保护方法中数据损失程度较大及数据可用性较低等方面问题,优化了k-同构算法和随机化算法,设计基于k-同构和局部随机化的隐私保护方法。实验结果表明:本文方法可以有效减少信息损失,保护用户隐私信息,对于衡量图谱中的调和平均最短距离、子图中心度2个方面均有较好效果,提高了发布数据可用性,可以抵御图结构信息背景知识再识别攻击。

In the process of social network data collection and release,privacy information leakage often takes place so that social network privacy protection has aroused people＇s concern.In order to solve the problem of significant data loss and poor availability of data in a single mode of social network privacy preserving method,ak-isomorphism and locally randomized method of privacy preserving is proposed,which included the optimized k-isomorphism method and locally randomized method.The experimental results show that the proposed method in this paper could effectively resist information loss,and has good performance in measurement of harmonic mean of the shortest distance and sub-graph centrality of information within graph.It can protect user＇s privacy information effectively and improve the usability of data issued,and also can effectively resist recognition attack from attacker based on background knowledge of graph structure.