Enhanced privacy preserving and truth discovery method for 5G and beyond vehicle crowd sensing systems

Abstract

Abstract As 5G networks are being deployed the key constraints of 5G networks have been highlighted, which encourages the probing research of 5G and beyond networks as the next-generation solutions. Especially for vehicular networks which require a network topology with very low latency and high reliability. 5G Vehicular networks offer higher reliability, lower latency, and secure and efficient communication services but these advancements come at the cost of new security and privacy concerns. Especially in the case of a high number of connected vehicles which leads to the concept of Vehicle Crowd Sensing System (VCS) in 5G enabled Vehicular Ad hoc networks (VANETS). Hence, due to this, the number of connected vehicle nodes is also soaring drastically which is directly proportional to the expansion of the Vehicle Crowd Sensing System (VCS). These vehicles are equipped with the latest gadgetries and a large number of sensors, which works simultaneously for processing at end-user utility applications e.g. navigation, predictions, and traffic monitoring. Therefore, in this scenario, all vehicle nodes upload their sole respective data to the cloud for computing which arise the need for reliability and privacy. Thus, Privacy-Preserving Truth Discovery (PPTD) is one such scheme that distills reliable information with privacy from raw signals acquired from different sources. Currently, PPTD brings substantial overhead in terms of consumed data, computation at the Vehicle level and requires each vehicle to remain online till the completion of uploading at the cloud server. So, in this paper, we explain a newly proposed novel Efficient Privacy-Preserving Truth Discovery (EPTD) entailing double masking for VCS and allowing Vehicles to go offline anytime during upload processing while ensuring privacy and accuracy at the same time. Simulation results have shown that the proposed work has outperformed all the previously existing methods.