社会网络隐私保护中K-同构算法研究

Research on K-isomorphism Algorithm for Social Network Privacy Preserving

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.

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.