关键词: 分布式, 动态社会网络, 隐私保护, Pregel-like, 预测链接

Abstract: Aiming at the low implementation efficiency and poor availability of data published in dynamic social network as well as processing large-scale dynamic social network graphs in a stand-alone workstation environment, this paper proposes a Distributed Dynamic Social Network Privacy-Preserving method Based on Link Prediction（D-DSNBLP）. This method realizes the parallel processing of anonymous large-scale graph data by the iterative updating model of message in Pregel-like. Firstly, the node is grouped by fast iteration. Secondly, the candidate node set is constructed in parallel according to the node attribute value in each group. Finally, a mutex set is built to add edges, to protect the privacy of nodes. Experiments show that the D-DSNBLP method improves the efficiency of processing large-scale dynamic social network and ensures the data availability of anonymous graphs.

Key words: distributed, dynamic social network, privacy protection, Pregel-like, link prediction