Anonymizing Social Networks
Anonymizing Social Networks
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DOI：
10.1201/9781420091502-c15
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Michael Hay;G. Miklau;David D. Jensen;Philipp Weis;Siddharth Srivastava
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文献类型：
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作者：
Michael Hay;G. Miklau;David D. Jensen;Philipp Weis;Siddharth Srivastava
技术的进步使收集关于个人和他们之间的联系的数据成为可能，例如电子邮件通信和友谊。收集了这类社交网络数据的机构和研究人员通常对允许其他人分析这些数据有着令人信服的兴趣。然而，在许多情况下，数据描述的关系是私人的(例如，电子邮件通信)，完全共享数据可能会导致不可接受的披露。本文提出了一个评估匿名网络数据共享的隐私风险的框架。这包括一个对手知识模型，我们考虑了几个变体，并与已知的图论结果建立了联系。在几个现实世界的社交网络上，我们证明了简单的匿名技术是不够的，即使是消息灵通的对手也会严重侵犯隐私。我们提出了一种新的基于网络扰动的匿名技术，并通过实验证明该技术可以显著降低隐私威胁。我们还分析了网络匿名化对社会网络分析数据的效用的影响。
Advances in technology have made it possible to collect data about individuals and the connections between them, such as email correspondence and friendships. Agencies and researchers who have collected such social network data often have a compelling interest in allowing others to analyze the data. However, in many cases the data describes relationships that are private (e.g., email correspondence) and sharing the data in full can result in unacceptable disclosures. In this paper, we present a framework for assessing the privacy risk of sharing anonymized network data. This includes a model of adversary knowledge, for which we consider several variants and make connections to known graph theoretical results. On several real-world social networks, we show that simple anonymization techniques are inadequate, resulting in substantial breaches of privacy for even modestly informed adversaries. We propose a novel anonymization technique based on perturbing the network and demonstrate empirically that it leads to substantial reduction of the privacy threat. We also analyze the eect that anonymizing the network has on the utility of the data for social network analysis.