Privacy-preserving task allocation for edge computing-based mobile crowdsensing. (January 2022)

• Record Type: Journal Article
• Authors: Ding, Xuyang; Lv, Ruizhao; Pang, Xiaoyi; Hu, Jiahui; Wang, Zhibo; Yang, Xu; Li, Xiong
• Is Part Of: Computers & electrical engineering. Volume 97(2022)
• Publication Date: 2022-01
• Subjects: Big data -- Crowdsensing -- Edge computing -- Task allocation -- Homomorphic encryption -- Privacy preservation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107528
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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. Graphical abstract: Highlights: Allocate task and collect data while protecting for data privacy of users. Protect users' location by a homomorphic encryption task allocation mechanism. Avoid the fraud problem of edge servers by secure payment mechanism.