Privacy Protection Method for k Degree Anonymity Based on Node Classification
摘要
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.