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Structural Attack to Anonymous Graph of Social Networks

机译：对社交网络匿名图的结构攻击

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摘要

With the rapid development of social networks and its applications, the demand of publishing and sharing social network data for the purpose of commercial or research is increasing. However, the disclosure risks of sensitive information of social network users are also arising. The paper proposes an effective structural attack to deanonymize social graph data. The attack uses the cumulative degree of n-hop neighbors of a node as the regional feature and combines it with the simulated annealing-based graph matching method to explore the nodes reidentification in anonymous social graphs. The simulation results on two social network datasets show that the attack is feasible in the nodes reidentification in anonymous graphs including the simply anonymous graph, randomized graph and κ-isomorphism graph.
机译：随着社交网络及其应用的快速发展，出于商业或研究目的而发布和共享社交网络数据的需求正在增长。但是，社交网络用户敏感信息的公开风险也随之增加。本文提出了一种有效的结构攻击来对社交图数据进行匿名处理。该攻击将节点的n跳邻居的累积程度作为区域特征，并将其与基于模拟退火的图匹配方法结合起来，以探索匿名社交图中节点的重新识别。在两个社交网络数据集上的仿真结果表明，该攻击在匿名图包括简单匿名图，随机图和κ同构图的节点重新识别中是可行的。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第14期|237024.1-237024.8|共8页
  • 作者

    Tieying Zhu; Shanshan Wang; Xiangtao Li; Zhiguo Zhou; Riming Zhang;

  • 作者单位

    School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China;

    School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China;

    School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China;

    School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China;

    School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China;

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