When Mobile Crowdsensing Meets Privacy
Zhibo Wang, Xiaoyi Pang, Jiahui Hu, Wenxin Liu, Qian Wang, Yanjun Li, Honglong Chen · IEEE Communications Magazine · 2019
Mobile crowdsensing (MCS) has now become an effective paradigm to collect massive data for various sensing applications. However, the interactions between mobile users and the platform, and the data release to third parties, pose severe challenges of privacy leakage for MCS systems, such as the leakage of users' identities and locations. Although several works on MCS have explored the privacy issues in task allocation, incentive, and data reporting, there is still a lack of a comprehensive privacy preserving framework for MCS to protect the privacy of users throughout users' involvement in crowdsensing tasks. In this article, we divide the life cycle of each crowdsensing task in MCS into four phases: task allocation, incentive, data collection, and data publishing, and design a privacy-preserving framework for MCS to protect users' privacy in the whole life cycle of MCS.