Privacy Protection Method for Sensitive Weighted Edges in Social Networks

被引:1
|
作者
Gong, Weihua [1 ]
Jin, Rong [2 ]
Li, Yanjun [1 ]
Yang, Lianghuai [1 ]
Mei, Jianping [1 ]
机构
[1] Zhejiang Univ Technol, Sch Comp Sci & Technol, Hangzhou 310023, Peoples R China
[2] Zhejiang Sci Tech Univ, Sch Informat & Elect, Hangzhou 310018, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Social Network; Privacy Protection; Sensitive Weighted Edges; Edge Betweenness;
D O I
10.3837/tiis.2021.02.009
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.
引用
收藏
页码:540 / 557
页数:18
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