移动群智感知中支持隐私保护的动态激励机制

针对移动群智感知中高质量感知数据与参与用户隐私之间的矛盾，提出一种支持隐私保护的动态激励机制。首先，采用轻量级隐私保护方法，利用安全加密哈希函数为竞标用户生成不少于256位的可变地址序列，并结合随机数对候选用户节点的效用报价进行隐匿和约束；其次，通过定义区域热度、时间热度、数据完整率和数据质量等多维参数，实现任务价值与用户效用报价的动态平衡；最后，依据用户提交的效用报价和任务预算，并利用逆向拍卖思想，完成对任务参与节点的最优选择和动态激励。在群智感知系统模拟平台上进行仿真实验，结果表明所提机制不仅增强了隐私保护度和数据精确度，同时提升了时间效率和激励效果。

Dynamic incentive mechanism with privacy-preserving in mobile crowd sensing

Aiming at the contradiction between high-quality perception data and user privacy in mobile crowd sensing system, this paper proposed a dynamic incentive mechanism with privacy-preserving. Firstly, it used the lightweight privacy-preserving method and a secure encryption hash function to generate a variable address sequence of not less than 256 bit for the bidding user, and integrated a random number to conceal and constraint the data utility price of the candidate user node. Secondly, by defining multi-dimensional parameters such as regional heat, time heat, data integrity rate and data quality, it realized the dynamic balance between task value and data utility price. Finally, based on the data utility price of the user and task budget, and using the reverse auction, it completed the optimal selection and dynamic incentive for the task participation nodes. Simulation experiments on the mobile crowd sensing system simulation platform show that the proposed mechanism not only enhances the privacy-preserving and data accuracy, but also improves the time efficiency and incentive effect.