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.

随着 5G 网络的部署，5G 网络的关键约束已经凸显，这鼓励了 5G 及以后网络作为下一代解决方案的探索性研究。特别是对于需要具有极低延迟和高可靠性的网络拓扑的车载网络。5G 车载网络提供更高的可靠性、更低的延迟以及安全高效的通信服务，但这些进步是以新的安全和隐私问题为代价的。特别是在大量联网车辆的情况下，这导致了支持 5G 的车辆自组织网络 (VANETS) 中的车辆人群感应系统 (VCS) 的概念。因此，因此，连接的车辆节点的数量也在急剧飙升，这与车辆人群感应系统（VCS）的扩展成正比。这些车辆配备了最新的小工具和大量传感器，它们同时工作以处理最终用户实用应用程序，例如导航、预测和交通监控。因此，在这种情况下，所有车辆节点都将各自的数据上传到云端进行计算，这就产生了对可靠性和隐私性的需求。因此，隐私保护真相发现 (PPTD) 就是这样一种方案，它从从不同来源获取的原始信号中提取具有隐私的可靠信息。目前，PPTD在消费数据、车辆级计算方面带来了大量开销，并且需要每辆车保持在线直到完成上传到云服务器。所以，在这篇论文中，我们解释了一种新提出的新型高效隐私保护真相发现 (EPTD)，它需要对 VCS 进行双重屏蔽，并允许车辆在上传处理过程中随时离线，同时确保隐私和准确性。仿真结果表明，所提出的工作优于所有先前存在的方法。