移动群智感知下的位置隐私保护与任务分配方案

Location Privacy Protection and Task Assignment Scheme Based on Mobile Swarm Intelligence Perception

在移动群智感知应用中,平台需要参与者的精确位置来优化任务分配,但用户在上传数据时,会泄露自身的位置隐私信息,同时,平台下发任务时,也会将任务点的具体位置暴露给攻击者,造成重要信息的泄露。针对这些问题,提出了一种基于差分隐私与属性加密的位置隐私保护与任务分配方案,考虑人员流动密度对任务分配的影响,在差分隐私约束下最小化参与者的移动距离,利用属性加密的方法下发任务区域,防止任务点的隐私泄露。经过实验仿真,所提方案能够在保证用户位置隐私信息的基础上降低用户位置混淆后带来的误差距离,同时方案整体也能保持较高的运行效率。

In mobile swarm perception applications,the platform needs the precise location of the participants to optimize task allocation,but users will leak their location privacy information when uploading data.At the same time,when the platform issues orders,the detailed location of the task point is exposed to attackers,resulting in the leakage of important information.Aiming at these problems,a location privacy protection and task allocation scheme based on differential privacy and attribute encryption is proposed.Considering the impact of personnel flow density on task allocation,the moving distance of the participants is minimized under the constraint of differential privacy.The task area is issued by the attribute encryption method to prevent the privacy leakage of the task point.After the experimental simulation,the proposed scheme can reduce the error distance caused by location confusion on the basis of ensuring user’s location privacy information,and at the same time it can maintain a high overall operating efficiency.