Privacy-preserving task allocation for edge computing-based mobile crowdsensing

被引：28

作者：

Ding, Xuyang ^{[1
]}

Lv, Ruizhao ^{[2
]}

Pang, Xiaoyi ^{[2
,4
]}

Hu, Jiahui ^{[3
]}

Wang, Zhibo ^{[3
]}

Yang, Xu ^{[1
]}

Li, Xiong ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China

[2] Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China

[3] Zhejiang Univ, Sch Cyber Sci & Technol, Hangzhou 310058, Zhejiang, Peoples R China

[4] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2022年 / 97卷

关键词：

Big data; Crowdsensing; Edge computing; Task allocation; Homomorphic encryption; Privacy preservation;

D O I：

10.1016/j.compeleceng.2021.107528

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the era of big data, edge computing has coped greatly with the increase in data. Recently, edge computing has been incorporated into mobile crowdsensing (MCS) to collect large-scale data, but existing edge computing-based MCS (EC-MCS) ideally assumes that edge servers are trusted. In this paper, a novel mechanism is proposed that we use semi-honest entities to securely and efficiently complete task assignment in large-scale crowdsensing. Firstly, homomorphic encryption is used to encrypt users' location information, and the collaboration between edge servers is used to complete task allocation under cipher-text. Then, the optimal users are selected to complete tasks and upload the encrypted sensing data. Moreover, a secure payment mechanism is proposed to avoid fraud problems in semi-honest edge servers. Finally, we analyze the security of our scheme theoretically and conduct a multi-dimensional simulation experiment to prove the effectiveness and availability of the proposed scheme.

引用

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