A Decentralized Location Privacy-Preserving Spatial Crowdsourcing for Internet of Vehicles

被引:52
|
作者
Zhang, Junwei [1 ]
Yang, Fan [1 ]
Ma, Zhuo [1 ]
Wang, Zhuzhu [1 ]
Liu, Ximeng [2 ,3 ]
Ma, Jianfeng [1 ]
机构
[1] Xidian Univ, Sch Cyber Engn, Xian 710071, Peoples R China
[2] Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China
[3] Fuzhou Univ, Key Lab Informat Secur Network Syst, Fuzhou 350108, Peoples R China
基金
中国国家自然科学基金;
关键词
Task analysis; Privacy; Encryption; Crowdsourcing; Data privacy; Internet of Vehicles; spatial crowdsourcing; location privacy; multi-level privacy-preserving; blockchain;
D O I
10.1109/TITS.2020.3010288
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
With the rapid development of Internet of Vehicles (IoV), vehicle-based spatial crowdsourcing (SC) applications have been proposed and widely applied to various fields. However, location privacy leakage is a serious issue in spatial crowdsourcing because workers who participate in a crowdsourcing task are required to upload their driving locations. In this paper, we propose a decentralized location privacy-preserving SC for IoV, which allows vehicle users to securely participate in SC with ensuring the task's location policy privacy and providing multi-level privacy preservation for workers' locations. Specifically, we introduce blockchain technology into SC, which can eliminate the control of vehicle user data by SC-server. We combine the additively homomorphic encryption and circle-based location verification to ensure the confidentiality of task's location policy. To achieve multi-level privacy preservation for workers' driving locations, we only reveal a grid where workers are located in. The size of the grid represents the level of privacy preservation. We leverage the order-preserving encryption and non-interactive zero-knowledge proof to prevent workers from illegally obtaining rewards by forging their driving locations. The security analysis results show that our framework can satisfy the above requirements. In addition, the experiment results demonstrate that our framework is efficient and feasible in practice.
引用
收藏
页码:2299 / 2313
页数:15
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