Mobile crowdsensing (MCS) has been an emerging technology thanks to the smart devices which are capable of sensing and computing to achieve large-scale, complex sensing tasks by cooperation. However, large-scale deployment might be impeded due to that fact that the participant may face the risk of privacy leakage, and if they are not compensated favorably, they may not be willing to contribute sensing capability. To overcome the above challenges, we propose an incentive mechanism for privacy-preserving mobile crowdsensing. More specifically, we introduce a trusted third party and combine partially blind signature, which can effectively reduce the correlation between participants and data and the number of interactions between users and task platform, so as to achieve high level participant privacy. In addition, considering data quality, we define some concepts including data quality relevance, user credit, location relevance and user utility, and design a Credit-based Incentive Mechanism (CIM) based on marginal benefit density and credit, in order to obtain the maximum benefit of a task platform under given budget. Extensive simulations are carried out to show that the proposed incentive mechanism achieves superior performance compared with state-of-the-art solutions. To the existing multi-stage incentive solutions, our proposed solution can achieve higher-quality data at the expense of less time efficiency.

由于智能设备能够感应和计算，通过合作实现大规模，复杂的感应任务，因此移动人群感应（MCS）成为一项新兴技术。但是，由于参与者可能面临隐私泄露的风险，因此可能会阻止大规模部署，并且如果没有对他们进行有利的补偿，他们可能不愿意贡献感知能力。为了克服上述挑战，我们提出了一种用于保护隐私的移动人群感知的激励机制。更具体地说，我们引入了受信任的第三方并结合了部分盲签名，可以有效地减少参与者与数据之间的相关性以及用户与任务平台之间的交互次数，从而实现高水平的参与者隐私。此外，考虑到数据质量，ç基于REDIT我ncentive中号基于边际效益的密度和信贷，以获得给定的预算下一个任务平台的最大好处echanism（CIM）。进行了广泛的仿真，结果表明，与最新解决方案相比，所提出的激励机制具有更高的性能。对于现有的多阶段激励解决方案，我们提出的解决方案可以以较少的时间效率为代价获得更高质量的数据。