Resisting re-identification mining on social graph data
Resisting re-identification mining on social graph data
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DOI：
10.1007/s11280-017-0524-3
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发表时间：
2018-11-01
影响因子：
3.7
通讯作者：
Wang, Jianxin
中科院分区：
文献类型：
--
作者：
Gao, Jianliang;Ping, Qing;Wang, Jianxin
各种敏感个人信息成为社交网络的隐私问题。然而，攻击者可以利用社交图的特征来重新识别社交网络的目标实体。在本文中，我们首先分析了一种名为基于bin的攻击的新攻击模型，该模型根据社交网络中的图结构特征重新识别社交个体。对于基于 bin 的攻击，我们提出了一种新颖的 k-匿名方案。通过该方案，社会个体得到完全的k-匿名保护。实验证明了该方案的有效性。匿名网络的实用性通过顶点度和介数的结果得到证明。
Varieties of sensitive personal information become a privacy concern for social networks. However, characteristics of social graphs could be utilized by attackers to re-identify target entities of social networks. In this paper, we first analyze a new attack model named bin-based attack, which re-identifies social individuals in social networks, according to their graph structure characteristics. For bin-based attack, we propose a novel k-anonymity scheme. With this scheme, social individuals are completely k-anonymity protection. Experiments illustrate the effectiveness of the proposed scheme. The utility of anonymized networks are demonstrated with the results of vertex degree, and betweenness.