基于节点分类的k度匿名隐私保护方法

针对传统k度匿名隐私保护方法严重破坏图结构和无法抵抗结构性背景知识攻击的问题,提出改进的k度匿名隐私保护方法。引入社区的概念,将节点划分为社区内节点和连接社区的边缘节点两类,通过区分不同节点的重要性,实现社区内节点的度匿名和边缘节点的社区序列匿名,从而完成整个社交网络的k度匿名。实验结果表明,该方法可降低数据实用性损失,抵抗以节点的度和节点所在社区关系为背景知识的攻击,提升隐私保护力度。

Privacy Protection Method for k Degree Anonymity Based on Node Classification

Existing k degree anonymous privacy protection methods usually damage the graph structure significantly and cannot resist structural background knowledge attacks.To address the problem,this paper proposes an improved k degree anonymous privacy protection method.The method introduces the concept of community,and divides nodes into two types which including nodes in the community and edge nodes that connect communities.The importance of nodes is differentiated,and the degree anonymity of the nodes in the community and the community sequence anonymity of the edge nodes are implemented,thereby the k degree anonymity of the entire social network is completed.Experimental results show that the proposed method reduces the practical loss of data,and can resist attacks that take node degree and community relationship as background knowledge.Thus,privacy protection is enhanced.