Privometer: Privacy protection in social networks
Privometer: Privacy protection in social networks
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Privometer：社交网络中的隐私保护
DOI：
10.1109/icdew.2010.5452715
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Mohamed Yakout
中科院分区：
文献类型：
--
作者：
N. Talukder;M. Ouzzani;A. Elmagarmid;Hazem Elmeleegy;Mohamed Yakout
Facebook和Orkut等社交网络的日益普及引发了一些隐私问题。通过隐藏敏感属性来保护个人信息隐私的传统方法已不再适用。研究表明，概率分类技术可以有效地推断出此类隐私信息。在这个过程中，被披露的朋友、团体关系甚至参与活动的敏感信息，如标签和评论，都被视为背景知识。在本文中，我们提出了一种隐私保护工具，称为Privometer，它可以测量用户配置文件中敏感信息的泄漏量，并建议自我清理操作来调节泄漏量。与先前的研究相反，在推理技术中使用公开可用的个人资料信息，我们考虑了一个增强模型，其中安装在用户朋友配置文件中的潜在恶意应用程序可以访问更多信息。在我们的模型中，仅仅隐藏敏感信息不足以保护用户隐私。我们提出了一个Privometer在Facebook上的实现。
The increasing popularity of social networks, such as Facebook and Orkut, has raised several privacy concerns. Traditional ways of safeguarding privacy of personal information by hiding sensitive attributes are no longer adequate. Research shows that probabilistic classification techniques can effectively infer such private information. The disclosed sensitive information of friends, group affiliations and even participation in activities, such as tagging and commenting, are considered background knowledge in this process. In this paper, we present a privacy protection tool, called Privometer, that measures the amount of sensitive information leakage in a user profile and suggests self-sanitization actions to regulate the amount of leakage. In contrast to previous research, where inference techniques use publicly available profile information, we consider an augmented model where a potentially malicious application installed in the user's friend profiles can access substantially more information. In our model, merely hiding the sensitive information is not sufficient to protect the user privacy. We present an implementation of Privometer in Facebook.