Secure and Privacy Preserving Protocol for Cloud-Based Vehicular DTNs

Cloud-assisted vehicular delay tolerant networks (DTNs) have been utilized in wide-ranging applications where a continuous end-to-end connection is unavailable, the message transmission is fulfilled by the cooperation among vehicular nodes and follows a store-carry-and-forward manner, and the complex computational work can be delegated to the disengaged vehicles in the parking lots which constitute the potential vehicular cloud. Nevertheless, the existing incentive schemes as well as the packet forwarding protocols cannot well model continuous vehicle collaboration, resist vehicle compromise attacks and collusion attacks, leaving the privacy preservation issues untouched. In this paper, a novel threshold credit-based incentive mechanism (TCBI) is proposed based on the modified model of population dynamics to efficiently resist the node compromise attacks, stimulate the cooperation among intermediate nodes, maximize vehicular nodes' interest, and realize the fairness of possessing the same opportunity of transmitting packets for credits. Then, a TCBI-based privacy-preserving packet forwarding protocol is proposed to solve the open problem of resisting layer-adding attack by outsourcing the privacy-preserving aggregated transmission evidence generation for multiple resource-constrained vehicles to the cloud side from performing any one-way trapdoor function only once. The vehicle privacy is well protected from both the cloud and transportation manager. Finally, formal security proof and the extensive simulation show the effectiveness of our proposed TCBI in resisting the sophisticated attacks and the efficiency in terms of high reliability, high delivery ratio, and low average delay in cloud-assisted vehicular DTNs.