社会网络隐私保护中K-同构算法研究

针对社会网络发布图数据面临的隐私泄露问题,提出了一种k-同构隐私保护算法.通过对原始图数据进行有效划分为k个子图,同时为降低匿名成本,增加与删除边数量近似相等,保证发布的图数据是k-同构的,有效阻止了攻击者基于背景知识的结构化攻击.通过真实数据集进行实验,结果表明算法具有高的有效性,能减少信息丢失,提高匿名质量.

Research on K-isomorphism Algorithm for Social Network Privacy Preserving

As traditional privacy-preserving technology can’t be directly applied to the social network data of higher dimension,to solve published graph data for social network facing the issues of privacy disclosure,a k-isomorphism privacy protection algorithm is proposed.By the original graph data is divided into k sub-graphs effectively,in order to reduce the cost of anonymity,the number of edges added edges approximately equal to the deleted,and ensure the release of the graph data is the k isomorphic,which effectively prevents the attacker based on a structural background knowledge attack.Real data set by experiment results show that the algorithm has high validity and can reduce the loss of information,also improve the quality of anonymity.