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

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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Article number3052840
Pages (from-to)1-7
Number of pages7
JournalIEEE Transactions on Intelligent Transportation Systems
DOIs
Publication statusE-pub ahead of print - 28 Jan 2021

Keywords

  • Base stations
  • Convergence
  • Cost function
  • multi-party computation
  • optimal consensus algorithm.
  • Privacy
  • Real-time systems
  • Roads
  • Speed advisory systems
  • Urban areas
  • vehicle networks

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