CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Online Publication Date2018-01-23
Print Publication Date2018-07
Permanent link to this recordhttp://hdl.handle.net/10754/627073
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AbstractThe vehicular announcement network is one of the most promising utilities in the communications of smart vehicles and in the smart transportation systems. In general, there are two major issues in building an effective vehicular announcement network. First, it is difficult to forward reliable announcements without revealing users' identities. Second, users usually lack the motivation to forward announcements. In this paper, we endeavor to resolve these two issues through proposing an effective announcement network called CreditCoin, a novel privacy-preserving incentive announcement network based on Blockchain via an efficient anonymous vehicular announcement aggregation protocol. On the one hand, CreditCoin allows nondeterministic different signers (i.e., users) to generate the signatures and to send announcements anonymously in the nonfully trusted environment. On the other hand, with Blockchain, CreditCoin motivates users with incentives to share traffic information. In addition, transactions and account information in CreditCoin are tamper-resistant. CreditCoin also achieves conditional privacy since Trace manager in CreditCoin traces malicious users' identities in anonymous announcements with related transactions. CreditCoin thus is able to motivate users to forward announcements anonymously and reliably. Extensive experimental results show that CreditCoin is efficient and practical in simulations of smart transportation.
CitationLi L, Liu J, Cheng L, Qiu S, Wang W, et al. (2018) CreditCoin: A Privacy-Preserving Blockchain-Based Incentive Announcement Network for Communications of Smart Vehicles. IEEE Transactions on Intelligent Transportation Systems: 1–17. Available: http://dx.doi.org/10.1109/TITS.2017.2777990.
SponsorsThis work was supported in part by NSFC under Grant 61672092 and Grant U1736114, in part by National Key R&D Program of China under Grant 2017YFB0802805, in part by ZTE Corporation Foundation under Grant K17L00190, in part by the Science and Technology on Electronic Information Control Laboratory under Grant K16GY00040, and in part by the Fundamental Research funds for the Central Universities of China under Grant K17JB00060, Grant K17JB00020, and Grant KKJB17033536. The Associate Editor for this paper was S. Djahel.