边缘辅助群智感知位置隐私保护多任务分配机制

为了解决群智感知中隐私泄露和多任务分配的问题，提出了一种边缘辅助群智感知位置隐私保护(EALP)多任务分配机制。首先，考虑群感知任务具有地理相近特征，利用改进的模糊聚类(FCM)算法对任务位置进行聚类组合，改进聚类数目指标，提高多任务分配的合理性。接着，为了防止云平台和感知用户之间的共谋，在任务分配阶段，提出一种位置隐私保护协议，在感知用户、云服务器和边缘节点之间部署同态加密，云感知平台能够安全地计算感知用户的移动距离，而不知道感知用户的位置和任务聚类中心位置。最后，提出了一种基于蚁群算法多任务分配优化方案，兼顾平台和感知用户两者利益，优化感知用户执行任务路径。实验结果表明，与同类方法相比，所提机制在保护位置隐私的前提下提高了任务完成率，降低了系统的感知成本和用户移动成本。

Edge-assisted crowdsensing location privacy protection multi-task allocation mechanism

To solve the problem of privacy leakage and multi-task allocation in crowdsensing, this paper proposed an edge assisted crowdsensing location privacy protection(EALP) multi-task allocation mechanism. Firstly, considering the geography of tasks, this paper used improved fuzzy clustering(FCM) algorithm to cluster tasks locations, improve the clustering index, and enhance the rationality of multi-task allocation. Secondly, to prevent collusion between the cloud and perceived users, it proposed a location privacy protection protocol in the task allocation phase. It deployed homomorphic encryption among the perceived users, cloud and edge nodes. The cloud could safely calculate the mobile distance of the perceived users without knowing their locations and the location of the task cluster center. Finally, it proposed a multi-task allocation optimization scheme based on ant colony algorithm, it balanced the interests of both platform and perceptive users by optimizing the path of execute tasks. Experiment results show that compared with similar methods, this proposed mechanism improves task completion rate while protecting location privacy, and reduces system perception costs and user mobility costs.