随着智能设备的普及与移动互联网的快速发展，基于移动感知与众包思想的群智感知技术近年来已成为研究热点。然而，鉴于用户参与和数据聚合的特点，群智感知系统面临着用户隐私（位置、敏感数据等）泄露的严峻挑战。现有的研究工作重点关注于如何解决服务器和参与者双方在最大化各自收益时的矛盾，以及如何激励用户提高参与积极性和数据质量，对于隐私泄露问题和用户的个性化隐私需求则研究不足。针对此问题，本项目从感知任务时空关联性和用户隐私需求差异化出发，将隐私保护思想融入群智感知系统，深入研究个性化隐私保护的群智感知关键技术，包括隐私保护的任务分配、隐私保护的用户激励、隐私保护的数据收集、以及隐私保护的数据发布。本项目的研究将解决群智感知系统隐私泄露的问题，构建支撑“任务分配--用户激励--数据收集--数据发布”的数据流转全生命周期的个性化隐私保护保障体系，推动群智感知系统的安全化和实用化。

With the ubiquity of mobile devices and the rapid development of networking, mobile crowdsensing has attracted more and more attentions, which can realize large-scale and fine-grained spatio-temporal data sensing and collection at anywhere with low cost. However, considering the features of participatory sensing and data aggregation, mobile crowdsensing systems face the severe challenge of privacy leakage, such as the leakage of participants’ locations and sensitive data. Existing works mainly focus on balancing the welfare of the platform and the payoff of the participants, and incentivizing users to improve participation and data quality. However, the problem of privacy leakage and the personalized requirements of users on privacy protection have not been extensively explored. In order to solve the problem, considering the spatio-temporal correlation of sensing tasks and the difference of users’ requirements on privacy protection, this project integrates privacy protection into mobile crowdsensing systems and studies the key technologies of mobile crowdsensing with personalized privacy protection guarantee. In particular, this project focuses on privacy-preserving task allocation, privacy-preserving user incentive, privacy-preserving data collection and privacy-preserving data publishing. The project will solve the privacy leakage problem and build a personalized privacy-preserving framework for the entire process of mobile crowdsensing systems from task allocation to data publishing, which will significantly put forward the application of secure mobile crowdsensing systems.