Title: Compromising location privacies for vehicles cloud computing

Authors: Chi Lin; Yi Wang; Shuang Wei; Danyang He; Jie Wang

Addresses: School of Software Technology, Dalian University of Technology, Dalian Liaoning, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian Liaoning, China ' School of Software Technology, Dalian University of Technology, Dalian Liaoning, China;Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian Liaoning, China ' School of Software Technology, Dalian University of Technology, Dalian Liaoning, China;Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian Liaoning, China ' School of Software Technology, Dalian University of Technology, Dalian Liaoning, China;Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian Liaoning, China ' School of Software Technology, Dalian University of Technology, Dalian Liaoning, China;Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian Liaoning, China

Abstract: In this paper, we propose an enhanced vehicular crowdsourcing localisation and tracking (EVCLT) scheme for mounting a trajectory tracking attack in vehicular cloud computing environment. In our scheme, crowdsourcing technique is applied to sample the location information of certain users. Then matrix completion technique is used to generate our predictions of the users' trajectories. To alleviate the error disturbance of the recovered location data, Kalman filter technique is implemented and the trajectories of certain users are recovered with accuracy. At last, extensive simulations are conducted to show the performance of our scheme. Simulation results reveal that the proposed approach is able to accurately track the trajectories of certain users.

Keywords: crowdsourcing; Kalman filter; matrix completion; trajectory tracking.

DOI: 10.1504/IJWGS.2018.088395

International Journal of Web and Grid Services, 2018 Vol.14 No.1, pp.88 - 105

Available online: 13 Nov 2017

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