TC: Small: Towards Automating Privacy Controls for Online Social Networks

对于今天数以百万计的互联网用户来说，控制Facebook和LinkedIn等在线社交网络(OSN)上的信息访问是一项艰巨的挑战。当前系统中的隐私控制不能为其用户提供必要级别的灵活性和可用性。MySpace和LinkedIn等一些系统允许用户对他们的个人资料授予全有或全无访问控制。虽然使用简单，但这些控件并不精确，很容易将数据泄露给非预期的接收者，或者阻止数据的合法共享。相比之下，像Facebook这样的OSN提供了极其强大的控件，不幸的是，对于大多数用户来说，这些控件太复杂了，无法配置。这项建议解决了对既强大又易于使用的隐私控制策略的需求。通过使用机器学习技术从观察到的用户行为自动推断用户偏好，该工作提供了简单而强大的隐私策略。这项工作还提出了“隐私透镜”，这是一种通过任何指定用户的访问控制查看用户信息来调试隐私策略的通用机制。这些技术解决方案将作为第三方应用程序在Facebook社交网络上实施。此外，从部署的应用程序收集的数据将提供证据，以验证或驳斥被称为“隐私悖论”的令人困惑的现象，即用户尽管对在线隐私表达了强烈的担忧，但几乎没有采取行动来保护他们的隐私。拟议的项目解决了一个对保护在线信息至关重要的重大问题。通过允许社交网络根据用户的行为“了解”用户想要什么，PI消除了管理隐私策略的复杂性，从而为非技术互联网用户提供了一种简单而直观的方式来定制他们的偏好。这项工作在使用机器学习技术来推断用户偏好方面是新颖的，并可以改变为各种互联网应用程序构建隐私策略的方式。通过从大型社交网络收集用户数据，该项目还将提供重要支持，以改善对用户在线隐私行为背后动机的理解。最后，这项拟议的工作将把复杂的实验网络研究技术与详细的人体研究结合起来，为社会科学家进行的传统实验增加一个额外的维度。

For millions of Internet users today, controlling information access on Online Social Networks (OSNs) such as Facebook and LinkedIn is a difficult challenge. Privacy controls in current systems do not provide the necessary level of flexibility and usability to their users. Some systems like MySpace and LinkedIn allow users to grant all-or-nothing access control to their profiles. While simple to use, these controls are imprecise and can easily leak data to unintended recipients or prevent the legitimate sharing of data. In contrast, OSNs like Facebook provide extremely powerful controls that are unfortunately too complex for most users to configure. This proposal addresses the need for privacy control policies that are both powerful and simple to use. The proposed work provides simple and powerful privacy policies by using machine learning techniques to automatically infer user preferences from observed user behavior. The work also proposes "privacy lenses," a generalized mechanism to debug privacy policies by viewing user information through the access controls of any specified user. These technical solutions will be implemented on the Facebook social network as a third-party application. In addition, the data gathered from the deployed application will provide evidence to either validate or refute the perplexing phenomenon known as the "privacy paradox," where users take little action to protect their privacy despite expressing strong concerns about online privacy.The proposed project addresses a significant problem fundamental to protecting online information. By allowing the social network to "learn" what users want based on their actions, the PIs remove the complexity of managing privacy policies, thereby giving non-technical Internet users a simple and intuitive way to customize their preferences. The work is novel in its use of machine learning techniques to infer user preferences, and can change the way privacy policies are constructed for a wide variety of Internet applications. By gathering user data from a large-scale social network, the project will also provide significant support to improve understanding of the motivations behind users actions concerning online privacy. Finally, the proposed work will integrate sophisticated experimental networking research techniques with detailed human studies, adding an additional dimension to traditional experiments performed by social scientists.