Vehicular networks have tremendous potential to improve road safety, traffic efficiency, and driving comfort, where cooperative vehicular safety applications are a significant branch. In cooperative vehicular safety applications, through the distributed data fusion for large amounts of data from multiple nearby vehicles, each vehicle can intelligently perceive the surrounding conditions beyond the capability of its own onboard sensors. Trust evaluation and privacy preservation are two primary concerns for facilitating the distributed data fusion in cooperative vehicular safety applications. They have conflicting requirements and a good balance between them is urgently needed. Meanwhile, the computation, communication, and storage overheads will all influence the applicability of a candidate scheme. In this paper, we propose a Lightweight Privacy-Preserving Trust Evaluation (LPPTE) scheme which can primely balance the trust evaluation and privacy preservation with low overheads for facilitating the distributed data fusion in cooperative vehicular safety applications. Furthermore, we provide exhaustive theoretical analysis and simulation evaluation for the LPPTE scheme, and the results demonstrate that the LPPTE scheme can obviously improve the accuracy of fusion results and is significantly superior to the state-of-the-art schemes in multiple aspects.

车载网络具有巨大的潜力，可以提高道路安全性，交通效率和驾驶舒适性，而协作性车载安全应用是其中一个重要的分支。在合作车辆安全应用中，通过分布式数据融合以获取来自附近多辆车辆的大量数据，每辆车都可以智能地感知周围环境，而其自身车载传感器的能力除外。信任评估和隐私保护是在协作车辆安全应用程序中促进分布式数据融合的两个主要问题。它们具有相互矛盾的要求，并且迫切需要它们之间的良好平衡。同时，计算，通信和存储开销都将影响候选方案的适用性。在本文中，我们提出了一种轻量级的隐私保护信任评估（LPPTE）方案，该方案可以在低开销的情况下主要平衡信任评估和隐私保护，以促进协作车辆安全应用中的分布式数据融合。此外，我们对LPPTE方案进行了详尽的理论分析和仿真评估，结果表明LPPTE方案可以明显提高融合结果的准确性，并且在多个方面均显着优于最新方案。