在线社交网络协同数据共享中的隐私冲突检测与解决

Hongxin Hu, Gail-Joon Ahn, Jan Jorgensen
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引用次数: 154

摘要

近年来,我们看到了在线社交网络(osn)的巨大增长。这些osn不仅为虚拟社会互动和信息共享提供了有吸引力的手段,但也引起了许多安全和隐私问题。虽然osn允许单个用户管理对其数据的访问,但它们目前没有提供任何机制来强制执行与多个用户相关的数据的隐私问题,其余的隐私侵犯在很大程度上未得到解决,并导致至少一个用户打算保密的信息的潜在泄露。在本文中,我们提出了一种实现osn共享数据协同隐私管理的方法。特别是,我们提供了一个系统的机制来识别和解决协作数据共享的隐私冲突。我们的冲突解决方案通过量化隐私风险和共享损失来表明隐私保护和数据共享之间的权衡。我们还讨论了我们方法的概念验证原型实现,作为Facebook应用程序的一部分,并提供了我们方法的系统评估和可用性研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting and resolving privacy conflicts for collaborative data sharing in online social networks
We have seen tremendous growth in online social networks (OSNs) in recent years. These OSNs not only offer attractive means for virtual social interactions and information sharing, but also raise a number of security and privacy issues. Although OSNs allow a single user to govern access to her/his data, they currently do not provide any mechanism to enforce privacy concerns over data associated with multiple users, remaining privacy violations largely unresolved and leading to the potential disclosure of information that at least one user intended to keep private. In this paper, we propose an approach to enable collaborative privacy management of shared data in OSNs. In particular, we provide a systematic mechanism to identify and resolve privacy conflicts for collaborative data sharing. Our conflict resolution indicates a tradeoff between privacy protection and data sharing by quantifying privacy risk and sharing loss. We also discuss a proof-of-concept prototype implementation of our approach as part of an application in Facebook and provide system evaluation and usability study of our methodology.
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