MPC-CSAS：用于实时隐私保护速度咨询系统的多方计算
MPC-CSAS: Multi-Party Computation for Real-time Privacy-preserving Speed Advisory Systems
摘要：
作为高级驾驶员辅助系统（ADAS）的一部分，已经提出了基于车速的速度咨询系统（CSAS），以针对特定应用目的（例如排放控制和能量管理）向一组车辆推荐共同的速度。随着车辆到车辆（V2 V）、车辆到基础设施（V2 I）技术和先进的控制理论的到位，最先进的CSAS可以被设计成以隐私保护和分散的方式获得最佳速度。然而，目前的方法只适用于特定的代价函数的车辆，其执行通常涉及许多算法迭代导致收敛时间长。因此，国家的最先进的设计方法是不适用于CSAS设计，需要实时决策。在本文中，我们解决这个问题，通过引入MPC-CSAS，多方计算（MPC）为基础的设计方法的隐私保护CSAS。我们提出的方法是简单的实施和适用于所有类型的代价函数的车辆。此外，我们的仿真结果表明，建议的MPC-CSAS可以实现非常有前途的系统性能，在一个算法迭代，而不需要使用额外的基础设施，一个典型的CSAS。
英文摘要：
As a part of Advanced Driver Assistance Systems (ADASs), Consensus-based Speed Advisory Systems (CSAS) have been proposed to recommend a common speed to a group of vehicles for specific application purposes, such as emission control and energy management. With Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) technologies and advanced control theories in place, state-of-the-art CSAS can be designed to get an optimal speed in a privacy-preserving and decentralized manner. However, the current method only works for specific cost functions of vehicles, and its execution usually involves many algorithm iterations leading long convergence time. Therefore, the state-of-the-art design method is not applicable to a CSAS design which requires real-time decision making. In this paper, we address the problem by introducing MPC-CSAS, a Multi-Party Computation (MPC) based design approach for privacy-preserving CSAS. Our proposed method is simple to implement and applicable to all types of cost functions of vehicles. Moreover, our simulation results show that the proposed MPC-CSAS can achieve very promising system performance in just one algorithm iteration without using extra infrastructure for a typical CSAS.
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