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Title: Achieving secure and privacy-preserving incentive in vehicular cloud advertisement dissemination
Authors: Kong, Qinglei
Lu, Rongxing
Zhu, Hui
Ma, Maode
Keywords: Advertisement Dissemination
Incentive Mechanism
Issue Date: 2018
Source: Kong, Q., Lu, R., Zhu, H., & Ma, M. (2018). Achieving secure and privacy-preserving incentive in vehicular cloud advertisement dissemination. IEEE Access, 6, 25040-25050.
Series/Report no.: IEEE Access
Abstract: The recent research trend of extending cloud computing to vehicles by leveraging under-utilized on-board capabilities [also known as vehicular cloud (VC)] is unsurprising, partly due to the increasing popularity of intelligent vehicles (with on-board computing equipment) and cloud computing. However, VC deployment is still challenging: how to securely verify responding vehicles, how to recruit competent vehicles, and how to achieve privacy preservation, are among the remaining research challenges. In this paper, we propose a secure and privacy-preserving incentive mechanism under the vehicular advertisement dissemination setting, which enables vehicles to opportunistically perform on-demand advertisement dissemination tasks and (financially) benefit from the completed task. Specifically, the road side unit (RSU) representing the advertisement dissemination server first announces a dissemination task, and performs the privacy-preserving access control of the responding vehicles. Then the RSU selects the participating vehicles among a pool of verified competent vehicles, and acknowledges the participation of selected vehicles with a secure secret sharing scheme; meanwhile, the selected vehicles performs the dissemination task with incentives. Finally, we prove the security of the proposed scheme in terms of secure access control and privacy preservation, and demonstrate its efficiency via simulation results (i.e., improves the amount of advertisement dissemination and brings incentives to participating vehicles).
DOI: 10.1109/ACCESS.2018.2827365
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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