MPC-CSAS: Multi-Party Computation for Real-Time Privacy-Preserving Speed Advisory Systems | IEEE Journals & Magazine | IEEE Xplore

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


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 vehicl...Show More

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.
Published in: IEEE Transactions on Intelligent Transportation Systems ( Volume: 23, Issue: 6, June 2022)
Page(s): 5887 - 5893
Date of Publication: 28 January 2021

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