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A Visual Model for Privacy Awareness and Understanding in Online Social Networks

机译:在线社交网络中隐私意识和理解的视觉模型

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The number of users participating in online social networks is increasing significantly recently. As a result, the amount of information created and shared by them is exploding. On one hand, sharing information online helps people stay in touch with each other, although virtually. But on the other hand, sharing too much information may lead to sensitive personal data being leaked unexpectedly. To protect their users' private information, online social network providers often employ technical methods like access control and cryptography among others. Although these approaches are good enough for their designated purposes, they provide little to no protection when are used wrongly. To reduce the number of mistakes users may make, online social network providers also offers them visual interfaces, instead of lengthy and boring texts, for privacy settings selection and configuration. Unfortunately, private information is stilled shared publicly, with or without its owners' awareness. In this paper, we attempt to mitigate the privacy leakage problem by proposing a novel visual model for measuring and representing users' privacy in online social network environment and associated privacy controller for protecting it. A concrete instance of the model has been designed and implemented. A demonstration of the model instance has been executed for one of the biggest social networks, Facebook. Initial results indicate the effectiveness of the proposed model and its concrete instance. However, a more important and difficult problem is whether online social network providers are willing to apply these results, which may affect sharing activities and go against their business objectives.
机译:参加在线社交网络的用户数最近正在增加。因此,它们创建和共享的信息的数量正在爆炸。一方面,在线分享信息有助于人们彼此保持联系,但几乎。但另一方面,共享太多信息可能导致敏感的个人数据意外泄露。为了保护他们的用户的私人信息,在线社交网络提供商通常采用技术方法,如访问控制和加密等。虽然这些方法足以满足其指定的目的,但它们在错误地使用时提供了很少的保护。为了减少用户可能的错误次数,在线社交网络提供商还为隐私设置选择和配置提供视觉接口,而不是冗长和无聊的文本。不幸的是,私人信息公开共享,有或没有其所有者的认识。在本文中,我们试图通过提出在在线社交网络环境中测量和代表用户隐私的新型视觉模型来缓解隐私泄漏问题,以及用于保护它的相关隐私控制器。设计和实施了模型的具体实例。模型实例的演示已为最大的社交网络,Facebook执行。初始结果表明所提出的模型及其具体实例的有效性。但是,更重要且困难的问题是在线社交网络提供商是否愿意应用这些结果,这可能影响共享活动并反对其业务目标。

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