CAREER: Enhancing the User Experience of Privacy Preference Specification

人们的计算机和移动设备提供的隐私设置是用户进行隐私管理的主要手段。与隐私相关的丑闻和争议不断涌现，突显了人们在理解和利用这些隐私设置来实现他们想要的隐私水平方面所面临的挑战。本研究旨在通过开发和测试技术来提高人们对隐私偏好规范的体验，从而克服这些挑战。需要开发和评估的技术包括尽量减少设置隐私偏好的工作量和复杂性，并使用户能够模仿可信专家的隐私设置。这些技术可以帮助非专业人员适当而有效地发现、理解和利用可用的隐私设置，从而显著地使系统操作与人们的隐私期望保持一致。本研究将研究增加隐私设置可用性的两种策略。第一种是通过将隐私管理任务分解为用户可管理的部分，对用户隐私设置进行优先级排序和自定义。二是利用专家社区对个人隐私保护的支持。该项目包括进行用户研究，为每项拟议技术的设计和开发提供信息。这些技术保留了人在循环中，并为自动化预测方法提供了有用的补充。研究结果将用于实施模型和原型，并将通过试点和实地研究进行评估和迭代改进。研究活动将与采用主动学习原则的教育组成部分相结合。学生参与课程和指导活动将产生与研究项目相关的见解，结果将纳入课程开发和课程设计。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The privacy settings provided by people's computers and mobile devices are the primary means by which users engage in privacy management. The constant stream of privacy related scandals and controversies highlight the challenges people face in understanding and utilizing these privacy settings to achieve the levels of privacy they desire. This research aims to overcome these challenges by developing and testing techniques to enhance the people's experience with their privacy preference specifications. The techniques to be developed and evaluated include minimizing the effort and complexity of setting privacy preferences and enabling user imitation of the privacy settings of trusted experts. These technique could help non-experts find, understand, and utilize available privacy settings appropriately and effectively, thus significantly aligning system operation with people's privacy expectations. This research will conduct research into two strategies for increasing the usability of privacy settings. The first is to prioritize and customize user privacy settings by breaking down privacy management tasks into manageable segments for users. The second is to leverage expert community support for individuals' privacy protection. The project involves conducting user studies that inform the design and development of each of the proposed techniques. These techniques retain the human-in-the-loop and provide a useful complement to automated predictive approaches. Findings from the research studies will be used to implement mockups and prototypes that will be evaluated and iteratively refined via pilots and field studies. The research activities will be integrated with educational components using active learning principles. Student involvement in courses and mentoring activities will generate insight related to the research project and the results will be incorporated in course development and curriculum design.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.