DDPLA: A Dynamic Differential Privacy Algorithm for Social Network Based on Local Community

Abstract

Social networks contain a large number of privacy information. Personal privacy will be jeopardized if network data without privacy protection is released directly. In view of the current privacy protection technology to protect the social network, there are some problems such as low maintenance of network structure or low accuracy of network data. In order to solve these problems, this paper proposes a dynamic differential privacy algorithm for social network based on local community (DDPLA). The algorithm can divide the social network into different communities, dynamically generate privacy budgets for different communities, and then generate uncertainty graphs. Experiments show that compared with other algorithms, the social network processed by DDPLA algorithm can better balance data utility and privacy protection. Furthermore, the algorithm can better protect important nodes.