Preserving the Privacy of Sensitive Relationships in Graph Data
Preserving the Privacy of Sensitive Relationships in Graph Data
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DOI：
10.1007/978-3-540-78478-4_9
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发表时间：
2007-08
期刊：
影响因子：
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通讯作者：
E. Zheleva;L. Getoor
中科院分区：
文献类型：
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作者：
E. Zheleva;L. Getoor
在本文中，我们专注于图数据中的敏感关系的隐私保护问题。我们将从匿名图数据中推断敏感关系的问题称为链接重新识别。我们提出了五种不同的隐私保护策略，这些策略在删除的数据量（以及它们的效用）和保留的隐私量方面有所不同。我们假设对手有一个准确的预测模型的链接，我们的实验表明不同的链接重新识别策略下的数据的不同结构特征的成功。
In this paper, we focus on the problem of preserving the privacy of sensitive relationships in graph data. We refer to the problem of inferring sensitive relationships from anonymized graph data aslink re-identification. We propose five different privacy preservation strategies, which vary in terms of the amount of data removed (and hence their utility) and the amount of privacy preserved. We assume the adversary has an accurate predictive model for links, and we show experimentally the success of different link re-identification strategies under varying structural characteristics of the data.