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首页> 外文期刊>International journal of parallel programming >Against Signed Graph Deanonymization Attacks on Social Networks
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Against Signed Graph Deanonymization Attacks on Social Networks

机译：反对社交网络上的签名图去匿名攻击

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摘要

Privacy protection is one of the most challenging problems of social networks. Simple removal of the labels is unable to protect the privacy of social networks because the information of graph structures can be utilized to deanonymize target nodes. Previous related proposals mostly assume that attacker knows only the target's neighborhood graph, but ignoring of signed edge attribute. The graph structure with signed edge attributes could cause serious privacy leakage of social networks. In this paper, we take the signed attribute of edges into account when achieving k-anonymity privacy protection for social networks. We propose a signed k-anonymity scheme to protect the privacy of key entities in social networks. With signed k-anonymity protection, these targets cannot be re-identified by attackers with confidence higher than 1/k. The proposed scheme minimizes the modification, which preserves high utility of the original data. Extensive experiments on real data sets and synthetic graph illustrate the effectiveness of the proposed scheme.
机译：隐私保护是社交网络最具挑战性的问题之一。简单地移除标签并不能保护社交网络的隐私，因为图结构的信息可以用来对目标节点进行匿名处理。先前的相关建议大多假设攻击者仅了解目标的邻域图，而忽略带符号的边缘属性。具有签名边缘属性的图结构可能会导致社交网络严重的隐私泄漏。在本文中，当实现社交网络的k匿名隐私保护时，我们考虑了edge的signed属性。我们提出了一个签名的k-匿名方案，以保护社交网络中关键实体的隐私。使用签名的k匿名保护，攻击者无法以高于1 / k的置信度重新标识这些目标。所提出的方案使修改最小化，从而保留了原始数据的高实用性。在真实数据集和合成图上的大量实验说明了该方案的有效性。

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