Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (22): 85-91.DOI: 10.3778/j.issn.1002-8331.1708-0394

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Distributed dynamic social network privacy-preserving method based on link prediction

ZHANG Xiaolin, YU Fangming, HE Xiaoyu, YUAN Haochen   

  1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
  • Online:2018-11-15 Published:2018-11-13



  1. 内蒙古科技大学 信息工程学院,内蒙古 包头 014010

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

摘要: 针对单机工作站环境下处理大规模动态社会网络图时执行效率低,以及动态社会网络发布中数据可用性较差的问题,提出基于预测链接的分布式动态社会网络隐私保护方法D-DSNBLP。该方法通过Pregel-like消息迭代更新模型,实现匿名大规模图数据的并行处理。首先通过快速迭代完成结点分组;其次根据各个组内的结点属性值并行构建候选结点集合;最后通过构建互斥边集合添加边,实现结点的隐私保护。实验表明,D-DSNBLP方法提高了大规模动态社会网络发布的效率,保证了匿名图的数据可用性。

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