TC: Small: Collaborative Research: User-centric Privacy Control for Collaborative Social Media

社交网站（如Facebook、MySpace、LinkedIn等）和其他在线协作工具已经成为人们发布和分享信息的地方。这种信息共享有很多好处，从实用（例如，共享商业文档）到纯粹的社交（例如，与远方的朋友交流）。同时，信息共享不可避免地对用户隐私构成重大威胁。例如，在社交网站上，记录在案的威胁包括身份盗窃、数字跟踪和个性化垃圾邮件。因此，越来越多的此类站点允许个人用户指定细粒度策略，以指示谁可以访问他们的数据，以及访问的程度。然而，研究一致表明，大多数最终用户发现为自己的数据指定访问控制策略的任务是压倒性的；因此，用户通常会跳过整个过程。这个项目的目标是帮助协作和社交媒体用户获得对他们数据的控制。为此，该项目将包括三个主要组成部分：辅助规范、反馈和改进建议。为了帮助用户最初为他们的数据指定访问控制策略，该项目将开发一个“隐私向导”，它采用数据挖掘和机器学习方法，包括主动学习，在用户输入最少的情况下构建准确的策略。为了提供关于现有隐私设置的反馈，该项目将采用两种方法：汇总分数和可视化。例如，聚合分数可以用来简洁地向用户解释她的设置与其他用户的设置有何不同。初步研究发现，项目反应理论（IRT）可以有效地用于这一目的。最后，该项目将考虑如何通过改进建议来丰富总体分数和视觉反馈，以帮助用户达到明确的社会曝光水平。在线协作工具和社交媒体在许多领域提供了巨大的希望。除了通过社交网站与朋友交流外，协作工具现在还用于商业、医学和教育等各个领域。然而，由于缺乏可用的隐私和访问控制，这些工具无法充分发挥其潜力。该项目的结果将通过原型实施和研究出版物传播。新的本科和研究生课程模块也将提高对该领域政策规范和新兴研究重要性的认识。

Social-networking sites (e.g., Facebook, MySpace, LinkedIn, etc.) and other online collaborative tools have emerged as places where people can post and share information. This information-sharing has many benefits, ranging from practical (e.g., sharing a business document) to purely social (e.g., communicating with distant friends). At the same time, information sharing inevitably poses significant threats to user privacy. In social-networking sites, for example, documented threats range from identity theft to digital stalking and personalized spam. As a result, a growing number of such sites allow individual users to specify fine-grained policies that indicate who can access their data, and to what extent. However, studies have consistently shown that most end-users find the task of specifying access-control policies for their own data overwhelming; as a result, users often skip the process altogether.The goal of this project is to help collaborative and social-media users gain control of their data. To that end, the project will include three main components: assisted specification, feedback, and refinement recommendations. To assist users in initially specifying access-control policies for their data, the project will develop a "privacy wizard," which employs data mining and machine learning methods, including active learning, to construct an accurate policy, with minimal input from the user. To provide feedback regarding existing privacy settings, the project will pursue two approaches: aggregate scores and visualizations. For example, an aggregate score can be used to concisely explain to the user how her settings differ from those of other users. Preliminary work found that Item Response Theory (IRT) can be used effectively for this purpose. Finally, the project will consider how aggregate scores and visual feedback can be enriched with recommendations for refinements to help the user achieve an expressed level of social exposure.Online collaborative tools and social media offer great promise in a number of arenas. In addition to communicating with friends via social networking sites, collaborative tools are now used in fields as diverse as business, medicine and education. However, the absence of usable privacy and access control prevents such tools from realizing their full potential. Results of this project will be disseminated via prototype implementations, as well as research publications. New undergraduate and graduate curriculum modules will also increase awareness of the importance of policy-specification and emerging research in this area.