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

TC：小型：实现在线社交网络隐私控制的自动化

• 批准号: 0916307
• 负责人: Ben Zhao
• 金额: $ 48.55万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916307&HistoricalAwards=false
• 关键词: TC; Small; Towards; Automating; Privacy

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.

对于当今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向其用户提供必要水平的灵活性和可用性。 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的访问控制。虽然使用简单，但这些控制并不精确，很容易将数据泄露给非预期的接收者或阻止合法的数据共享。相比之下，像Facebook这样的OSN提供了非常强大的控件，不幸的是，这些控件对于大多数用户来说太复杂了。 该提案满足了对既强大又易于使用的隐私控制策略的需求。 拟议的工作提供了简单而强大的隐私政策，通过使用机器学习技术，自动推断用户的偏好，从观察到的用户行为。 这项工作还提出了“隐私镜头”，一种通用的机制，通过查看任何指定用户的访问控制的用户信息来调试隐私策略。 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，从部署的应用程序中收集的数据将提供证据，以验证或反驳被称为“隐私悖论”的令人困惑的现象，即用户尽管对在线隐私表示强烈关注，但却很少采取行动来保护自己的隐私。通过允许社交网络根据用户的行为“学习”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，并且可以改变为各种互联网应用构建隐私策略的方式。 通过从大规模社交网络收集用户数据，该项目还将提供重要支持，以更好地了解用户涉及在线隐私的行为背后的动机。 最后，拟议的工作将把复杂的实验网络研究技术与详细的人类研究相结合，为社会科学家进行的传统实验增加了一个额外的维度。