A Framework for Computing the Privacy Scores of Users in Online Social Networks
A Framework for Computing the Privacy Scores of Users in Online Social Networks
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DOI：
10.1145/1870096.1870102
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发表时间：
2010-12-01
影响因子：
3.6
通讯作者：
Terzi, Evimaria
中科院分区：
文献类型：
--
作者：
Liu, Kun;Terzi, Evimaria
大量的工作致力于解决与社交网络相关的企业规模的隐私问题。这项工作的大部分集中在如何共享组织拥有的社交网络，而不透露身份或所涉及的用户的敏感关系。在这篇文章中，我们从个人用户的角度来探讨在线社交网络中提出的隐私问题：我们提出了一个框架来计算用户的隐私评分。该分数表示用户因参与网络而导致的潜在风险。我们对隐私评分的定义满足以下直观属性：用户披露的敏感信息越多，他或她的隐私风险就越高。此外，所披露的信息在网络中越明显，隐私风险就越高。我们开发数学模型来估计信息的敏感性和可见性。我们将我们的方法应用于合成和真实世界的数据，并证明其有效性和实用性。
A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their daily information-sharing activities.In this article, we approach the privacy issues raised in online social networks from the individual users' viewpoint: we propose a framework to compute the privacy score of a user. This score indicates the user's potential risk caused by his or her participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive information a user discloses, the higher his or her privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility.