Compromising location privacies for vehicles cloud computing. (2018)

• Record Type: Journal Article
• Authors: Lin, Chi; Wang, Yi; Wei, Shuang; He, Danyang; Wang, Jie
• Is Part Of: International journal of web and grid services. Volume 14:Number 1(2018)
• Page Start: 88
• Page End: 105
• Publication Date: 2018
• Subjects: crowdsourcing -- Kalman filter -- matrix completion -- trajectory tracking; Web services -- Periodicals; Computational grids (Computer systems) -- Periodicals; 006.78
• Languages: English
• ISSNs: 1741-1106

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.