Participant Privacy in Mobile Crowd Sensing Task Management

Abstract

Mobile crowd sensing enables a broad range of novel applications by leveraging mobile devices and smartphone users worldwide. While this paradigm is immensely useful, it involves the collection of detailed information from sensors and their carriers (i.e. participants) during task management processes including participant recruitment and task distribution. Such information might compromise participant privacy in various regards by identification or disclosure of sensitive attributes -- thereby increasing vulnerability and subsequently reducing participation. In this survey, we identify different task management approaches in mobile crowd sensing, and assess the threats to participant privacy when personal information is disclosed. We also outline how privacy mechanisms are utilized in existing sensing applications to protect the participants against these threats. Finally, we discuss continuing challenges facing participant privacy-preserving approaches during task management.