Critical Node Privacy Protection Based on Random Pruning of Critical Trees

Abstract

With the popularity of social networks, the conflict between the heterogeneous social network data publishing and user privacy leakage is becoming very obvious. Especially for critical node users, if the critical node users suffer from background knowledge attacks during data publishing, it can not only lead to the privacy information leakage of the critical user but also lead to the privacy leakage of their friends. To address this issue, we propose a critical node privacy protection method based on random pruning of critical trees. First, we obtain the critical node candidate set by the degree centrality. Then, we calculate the candidate node’s global and local criticality to get the critical node set. Next, we extract the critical tree with the critical node as the root node. Finally, we design a critical tree privacy protection strategy based on random pruning. The experimental results show that the proposed method can balance the privacy and availability of critical nodes in the network data publishing.