D-GSPerturb: A Distributed Social Privacy Protection Algorithm based on Graph Structure Perturbation

10.3966/199115992017102805005

Xiao-lin Zhang；Wen-chao Zhang；Chen Zhang；Li-Xin Liu；Xiao-Yu He

big data ； D-GSPerturb ； edge random perturbation ； privacy protection ； social network

電腦學刊

28卷5期（2017 / 10 / 01）

51 - 61

英文

The traditional privacy protection algorithm does not meet actual application requirements of processing large-scale graph data in terms of efficiency or capability. D-GSPerturb is a distributed social privacy protection algorithm based on graph structure perturbation; it is proposed to solve link privacy issues in social networks. The present vertex-centric algorithm can search large-scale social networks for reachable vertexes, transfer reachable information, and randomly perturb edges through between-vertex messaging, vertex value updating, and multi-iteration in programming. The experimental results show that D-GSPerturb not only improves the processing speed of large-scale graph data but also ensures the privacy protection effect and availability of data published.