In recent years, the development of the Internet of Vehicles (IoV) has led to an increase in the demand for data sharing services for the IoV. In the era of big data, safe and convenient data sharing services for IoV are inseparable from the support of reliable data. With the increase in the number of smart cars, how to filter the data provided by vehicles while protecting privacy to improve the quality of data sharing services has attracted the attention of scholars. However, in the existing data sharing scheme for IoV, the vehicles that provide data are selected based on the reputation mechanism or the voting mechanism. Our analysis shows that these two mechanisms lack objectivity and are vulnerable to cooperative attack. Therefore, based on spatiotemporal matching, a new trusted relationship establishment method, this paper proposes an objective and cooperative attack-resistant vehicle selection scheme for data sharing in IoV. Bloom filters and an exponential ElGamal encryption scheme are used to implement private set intersection cardinality (PSI-CA) technology for evaluating spatiotemporal matching level. In this paper, the scheme proposed is of great significance to improve existing vehicle services and improve driving safety.

中文翻译：

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基于PSI-CA的车联网数据共享选车方案

近年来，车联网的发展带动了车联网数据共享服务的需求增加。大数据时代，安全便捷的车联网数据共享服务离不开可靠数据的支撑。随着智能汽车数量的增加，如何在保护隐私的同时对车辆提供的数据进行过滤，以提高数据共享服务的质量引起了学者们的关注。然而，在现有的车联网数据共享方案中，提供数据的车辆是基于信誉机制或投票机制来选择的。我们的分析表明，这两种机制缺乏客观性，容易受到协同攻击。因此，基于时空匹配的一种新的信任关系建立方法，本文提出了一种客观、协作的抗攻击车辆选择方案，用于车联网数据共享。布隆过滤器和指数 ElGamal 加密方案用于实现私有集交集基数 (PSI-CA) 技术，用于评估时空匹配水平。本文提出的方案对于改善现有车辆服务、提高行车安全具有重要意义。