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PriMe: Human-centric privacy measurement based on user preferences towards data sharing in mobile participatory sensing systems

Mobile participatory sensing systems allow people with mobile devices to collect, interpret, and share data from their respective environments. One of the main obstacles for long-term participation in such systems is the users' privacy concerns. Due to the nature of these systems, users have to agree to provide some personalized information. Typically, however, people are reluctant to share any information, as it may be sensitive. This is especially the case if the content of the data in question is not completely transparent. In order to increase users' willingness to participate in such systems, we should help users identify which data they can share without violating their personal privacy policies. However, the perception of how sensitive a piece of information is may differ from user to user. In this paper, we propose the human-centric privacy measurement method PriMe, which quantifies privacy risks based on user preferences towards data sharing in participatory sensing systems. Further, we implemented and deployed PriMe in the real world as a user study for evaluation. The study shows that PriMe provides accurate ratings that fit users' individual perceptions of privacy, and is accepted by users as a trustworthy tool.