近年来，作为一种新兴的数据收集技术，移动群智感知受到广泛关注。它主要利用大家经常携带的移动智能终端（如：智能手机、可穿戴设备等）作为基本的感知单元，来完成大规模的社会感知任务。随着复杂群智感知应用的不断涌现，隐私泄漏事件频频发生，且机会式数据传输机制的低传输效率已成为制约该类应用发展的主要瓶颈之一。为此，本项目将研究支持隐私保护的机会式数据传输机制，具体工作包括：分别构造（1）细粒度隐私保护算法和（2）智能化的隐私保护方案，以解决数据传输中隐私泄露问题；（3）构建按需的激励机制，以解决数据传输中中间节点数目不足的问题；（4）研究本地化社交属性分析，以解决社交属性的本地计算困难的问题；（5）在上述基础上，设计基于社交属性的数据传输算法，以提高数据的传输效率。本项目研究将为移动群智感知应用的数据高效传输提供重要的理论依据和技术支撑。

In recent years, as an emerging data collection technology, the mobile crowd sensing uses smart devices, such as: smart phones, wearable devices, etc., as a fundamental sensing unit to finish large-scale social sensing tasks in a cooperation way. Thus, it has attracted more and more attention from academia and industry. With the continuous emergence of complex mobile crowd sensing applications, the data transmission mechanism has been one of the main bottlenecks restricting the development of these applications. Its primary problem is the low efficiency of data transmission. To this end, this project shall be focus on the opportunistic data transmission mechanism with privacy protection. More specifically, this project mainly concentrates on the study of the following problems. (1) How to design fine-grained privacy preserving algorithms? (2) How to construct intelligent privacy protection scheme? (3) How to build an on-demand incentive mechanism? (4) How to implement the localized social attribute analysis? (5) How to design a social aware data transmission algorithm to maximize the data transmission revenue. All these works will provide a strong theoretical basis for improving the efficiency of data transmission for mobile crowd sensing applications.