摘要
提出了一种基于图结构扰乱的分布式个性化社会网络隐私保护方法 DP-GSPerturb(Distributed personalized graph structure perturbation).该方法在分布式环境下,以节点为中心,通过节点间消息传递和节点值更新,查找敏感源节点的可达节点,传递可达信息给敏感源节点,随机扰乱敏感源节点的连接关系,实现敏感连接关系的个性化隐私保护.实验结果表明,DP-GSPerturb方法提高了处理大规模社会网络图数据的效率和数据发布的可用性.
DP-GSPerturb is a distributed personalized social privacy protection method based on graph structure perturbation; it is proposed to solve sensitive link privacy issues in personalized social networks. The method is a node-centric method that searches reachable nodes of sensitive source nodes, transfers reachable information to sensitive source nodes, and randomly perturbs links of sensitive source nodes through between-node messaging, node value updating to achieve the personalized privacy protection of sensitive link in the distributed environment. The experimental results show that DP-GSPerturb improves not only the processing speed of large-scale graph data but also the availability of data published.
出处
《微电子学与计算机》
CSCD
北大核心
2017年第6期72-77,83,共7页
Microelectronics & Computer
基金
国家自然科学基金项目(61562065)
关键词
分布式
社会网络
个性化隐私保护
图结构扰乱
边隐私
distributed
social network
personalized privacy protection
graph structure perturbation
link privacy
