In the context of evolving smart cities and autonomous transportation systems, Vehicular Ad-hoc Networks (VANETs) and the Internet of Vehicles (IoV) are growing in significance. Vehicles are becoming more than just a means of transportation; they are collecting, processing, and transmitting massive amounts of data to make driving safer and more convenient. However, this advancement ushers in complex issues concerning the centralized structure of traditional vehicular networks and the privacy and security concerns around vehicular data. This paper offers a novel, game-theoretic network architecture to address these challenges. Our approach decentralizes data collection through distributed servers across the network, aggregating vehicular data into spatio-temporal maps via secure multi-party computation (SMPC). This strategy effectively reduces the chances of adversaries reconstructing a vehicle's complete path, increasing privacy. We also introduce an economic model grounded in game theory that incentivizes vehicle owners to participate in the network, balancing the owners' privacy concerns with the monetary benefits of data sharing. This model aims to maximize the data consumer's utility from the gathered sensor data by determining the most suitable payment to participating vehicles, the frequency in which these vehicles share their data, and the total number of servers in the network. We explore the interdependencies among these parameters and present our findings accordingly. To define meaningful utility and loss functions for our study, we utilize a real dataset of vehicular movement traces.

翻译：在智慧城市和自动驾驶交通系统不断发展的背景下，车载自组织网络（VANETs）和车联网（IoV）的重要性日益凸显。车辆正超越单纯的交通工具角色，开始收集、处理并传输海量数据以提升驾驶的安全性与便捷性。然而，这一进步引发了诸多复杂问题，包括传统车载网络的集中式结构，以及车辆数据相关的隐私与安全顾虑。本文提出一种新颖的基于博弈论思想的网络架构来应对这些挑战。我们的方法通过网络中的分布式服务器实现数据收集的去中心化，并借助安全多方计算（SMPC）将车辆数据聚合为时空地图。该策略有效降低了攻击者重构车辆完整行驶路径的可能性，从而增强了隐私保护。我们还引入一种基于博弈论的经济模型，用以激励车主参与网络，在车主的隐私关切与数据共享的货币收益之间取得平衡。该模型旨在通过确定向参与车辆的最优支付金额、车辆共享数据的频率以及网络中的服务器总数，来最大化数据消费者从所收集传感器数据中获得的效用。我们探讨了这些参数之间的相互依赖关系，并据此展示我们的研究结果。为定义研究中具有实际意义的效用函数和损失函数，我们使用了车辆移动轨迹的真实数据集。