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Social Network Privacy for Attribute Disclosure Attacks

机译:针对属性披露攻击的社交网络隐私

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Increasing research on social networks stresses the urgency for producing effective means of ensuring user privacy. Represented ubiquitously as graphs, social networks have a myriad of recently developed techniques to prevent identity disclosure, but the equally important attribute disclosure attacks have been neglected. To address this gap, we introduce an approach to anonymize social networks that have labeled nodes, $alpha$-proximity, which requires that the label distribution in every neighbourhood of the graph be close to that throughout the entire network. We present an effective greedy algorithm to achieve $alpha$-proximity and experimentally validate the quality of the solutions it derives.
机译:越来越多的社交网络研究强调了迫切需要开发出有效的方法来确保用户隐私。社交网络无所不在地被表示为图形,它具有无数最新开发的技术来防止身份泄露,但是同样重要的属性泄露攻击也被忽略了。为了解决这一差距,我们引入了一种对带有标签节点$ alpha $ -proximity的社交网络进行匿名处理的方法,该方法要求图的每个邻域中的标签分布与整个网络中的标签分布接近。我们提出一种有效的贪心算法,以实现$ alpha $ -proximity并通过实验验证其得出的解决方案的质量。

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