Preserving privacy in social networks based on d-neighborhood subgraph anonymity
Preserving privacy in social networks based on d-neighborhood subgraph anonymity
复制标题
基于 d 邻域子图匿名的社交网络隐私保护
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Ju Shi
中科院分区：
文献类型：
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作者：
Ju Shi
在社交网络信息发布中，隐私保护是非常必要的，因为社交网络分析可能会侵犯个人隐私。提出了一种用超边矩阵描述的d-邻域子图的k-匿名模型，将子图的匿名化转化为匹配表示顶点的d-邻域子图的矩阵，并确保每个顶点同构的d-邻域子图的个数不小于k。实验结果表明，该模型能够有效地抵抗邻域攻击，保护隐私信息。
Preserving privacy is very necessary for social network information publishing,because analysis of social networks can violate the individual privacy.This paper proposed a k-anonymity model of d-neighborhood subgraph described by matrix of supe-edge.It transformed the anonymization of subgraph into matching the matrix which represented the d-neighborhood subgraph of vertex,and ensured that the numbers of isomorphic d-neighborhood subgraph was no less than k for every vertex.Experimental results show that the proposed model can effectively resist neighborhood attacks and preserve privacy information.