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.
CAREER: Enhancing the User Experience of Privacy Preference Specification