Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (9): 2678-2686.DOI: 10.11772/j.issn.1001-9081.2020111787

Special Issue: 网络与通信

• Network and communications • Previous Articles     Next Articles

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

LI Zhuo1,2, SONG Zihui2, SHEN Xin2, CHEN Xin2   

  1. 1. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research(Beijing Information Science and Technology University), Beijing 100101 China;
    2. Computer School, Beijing Information Science and Technology University, Beijing 100101 China
  • Received:2020-11-16 Revised:2021-01-18 Online:2021-09-10 Published:2021-05-12
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61872044), the Beijing Municipal Program for Top Talent, the Beijing Municipal Program for Top Talent Cultivation (CIT& TCD 201804055), the Opening Project of Beijing Key Laboratory of Internet Culture and Digital Dissemination Research.


李卓1,2, 宋子晖2, 沈鑫2, 陈昕2   

  1. 1. 网络文化与数字传播北京市重点实验室(北京信息科技大学), 北京 100101;
    2. 北京信息科技大学 计算机学院, 北京 100101
  • 通讯作者: 李卓
  • 作者简介:李卓(1983-),男,河南南阳人,副教授,博士,CCF会员,主要研究方向:移动无线网络、分布式计算;宋子晖(1993-),男,河南安阳人,硕士研究生,CCF会员,主要研究方向:移动群智感知;沈鑫(1998-),女,北京人,硕士研究生,主要研究方向:边缘计算;陈昕(1965-),男,江西南昌人,教授,博士,CCF会员,主要研究方向:网络性能评价、网络安全。
  • 基金资助:

Abstract: 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.

Key words: Mobile Crowd Sensing (MCS), Local Differential Privacy (LDP), edge computing, data availability, privacy protection

摘要: 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%。

关键词: 移动群智感知, 本地差分隐私, 边缘计算, 数据可用性, 隐私保护

CLC Number: