边缘计算支持下的移动群智感知本地差分隐私保护机制

Local differential privacy protection mechanism for mobile crowd sensing with edge computing

针对移动群智感知(MCS)中在用户数据提交阶段的隐私保护困难和因隐私保护造成成本增加的问题,基于本地差分隐私(LDP)保护原理设计出用户提交数据属性联合隐私保护的CS-MVP算法和用户提交数据属性独立隐私保护的CS-MAP算法。首先,基于属性关系构建用户提交数据的隐私性模型和任务数据的可用性模型,利用CS-MVP和CS-MAP算法解决隐私性约束下的可用性最大化问题;并且在边缘计算支持的MCS场景中,构建用户提交数据隐私保护下的三层MCS架构。理论分析证明了两个算法分别在数据属性联合隐私约束下和数据属性独立隐私约束下的最优性。实验结果表明,在相同隐私预算和数据量下,相较于LoPub和PrivKV,基于CS-MVP和CS-MAP算法的用户提交数据恢复正确感知数据的准确率分别平均提高了26.94%、84.34%和66.24%、144.14%。

Aiming at the problem of the difficulty in privacy protection and the cost increase caused by privacy protection in the user data submission stage in Mobile Crowd Sensing(MCS),CS-MVP algorithm for joint privacy protection and CS-MAP algorithm for independent privacy protection of the attributes of user submitted data were designed based on the principle of Local Differential Privacy(LDP).Firstly,the user submitted privacy model and the task data availability model were constructed on the basis of the attribute relationships.And CS-MVP algorithm and CS-MAP algorithm were used to solve the availability maximization problem under the privacy constraint.At the same time,in the edge computing supported MCS scenarios,the three-layer architecture for MCS under privacy protection of the user submitted data was constructed.Theoretical analysis proves the optimality of the two algorithms under the data attribute joint privacy constraint and data attribute independent privacy constraint respectively.Experimental results show that under the same privacy budget and amount of data,compared with LoPub and PrivKV,the accuracy of user submitted data recovered to correct sensor data based on CS-MVP algorithm and CS-MAP algorithm is improved by 26.94%,84.34%and 66.24%,144.14%respectively.