MPC-CSAS: Multi-Party Computation for Real-Time Privacy-Preserving Speed Advisory Systems

Mingming Liu,Ying Wang,Noel E O'Connor,Yingqi Gu,Qingzhi Liu,Long Cheng

doi:10.1109/tits.2021.3052840

Abstract

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 article, 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.

Highlights

AND RELATED WORKwe briefly introduce the optimization problem of Consensus-based Speed Advisory Systems (CSAS) as well as the related works.A
The proposed method, MultiParty Computation (MPC)-CSAS, has several advantages compared to the state-of-the-art DP-CSAS: (1) it is applicable to all emission cost functions; (2) it is simple to implement in real-time without imposing a large communication burden on the existing infrastructures; and (3) it can be deployed in a static and strongly connected network, but it can be extended to deal with weakly and dynamic connectivity conditions in a practical Intelligent Transportation Systems (ITS) scenario under a certain assumptions
If a vehicle is driving too fast/slow on a road and it cannot receive the message from a given base station, it is still possible for the vehicle to join a different group of vehicles implementing the MPC-CSAS on another stretch of road

Summary

INTRODUCTION

In cities, and this can bring some obvious benefits to various types of road users, such as reduced emissions (with less frequent accelerations/decelerations), reduced energy consumption, increased throughput, and increased safety and health [6]–[10]. Along this line, our previous work in [5] has attempted to devise an optimal speed for a CSAS in a privacy-preserving manner, namely without revealing in-vehicle information to other vehicles or to infrastructures. The proposed method, MPC-CSAS, has several advantages compared to the state-of-the-art DP-CSAS: (1) it is applicable to all emission cost functions; (2) it is simple to implement in real-time without imposing a large communication burden on the existing infrastructures; and (3) it can be deployed in a static and strongly connected network, but it can be extended to deal with weakly and dynamic connectivity conditions in a practical ITS scenario under a certain assumptions.

CSAS Problem Statement