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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Preserving Privacy with Probabilistic Indistinguishability in Weighted Social Networks
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Preserving Privacy with Probabilistic Indistinguishability in Weighted Social Networks

机译：通过加权社交网络中的概率不可区分性保护隐私

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摘要

The increasing popularity of social networks has inspired recent research to explore social graphs for marketing and data mining. As social networks often contain sensitive information about individuals, preserving privacy when publishing social graphs becomes an important issue. In this paper, we consider the identity disclosure problem in releasing weighted social graphs. We identify weighted 1*-neighborhood attacks, which assume that an attacker has knowledge about not only a target's one-hop neighbors and connections between them (1-neighborhood graph), but also related node degrees and edge weights. With this information, an attacker may re-identify a target with high confidence, even if any node's 1-neighborhood graph is isomorphic with k−1 other nodes’ graphs. To counter this attack while preserving high utility of the published graph, we define a key privacy property, probabilistic indistinguishability, and propose a heuristic indistinguishable group anonymization (HIGA) scheme to anonymize a weighted social graph with such a property. Extensive experiments on both real and synthetic data sets illustrate the effectiveness and efficiency of the proposed scheme.
机译：社交网络的日益普及激发了最近的研究，以探索用于营销和数据挖掘的社交图。由于社交网络通常包含有关个人的敏感信息，因此在发布社交图谱时保护隐私变得很重要。在本文中，我们考虑了发布加权社会图时的身份披露问题。我们确定加权的1 *邻居攻击，假设攻击者不仅了解目标的一跳邻居和它们之间的连接（1-邻居图），而且了解相关的节点度和边缘权重。有了这些信息，即使任何节点的1邻域图与k-1个其他节点的图同构，攻击者也可以高信度地重新识别目标。为了在保持已发布图表的高度实用性的同时应对这种攻击，我们定义了关键的隐私属性，即概率不可区分性，并提出了启发式不可区分组匿名化（HIGA）方案，以对具有此类属性的加权社交图进行匿名化。在真实和综合数据集上的大量实验说明了该方案的有效性和效率。

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