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

doi:10.1109/TITS.2021.3052840

	MPC-CSAS: Multi-Party Computation for Real-Time Privacy-Preserving Speed Advisory Systems
	Liu, Mingming 1; Cheng, Long 2; Gu, Yingqi 3; Wang, Ying 4; Liu, Qingzhi 5; O'Connor, Noel E.1,6
	2021-01-28
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
页码	7
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 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.
关键词	Cost function Urban areas Real-time systems Roads Privacy Convergence Base stations Speed advisory systems multi-party computation vehicle networks optimal consensus algorithm
DOI	10.1109/TITS.2021.3052840
收录类别	SCI
语种	英语
资助项目	Science Foundation Ireland[SFI/12/RC/2289_P2]
WOS研究方向	Engineering ; Transportation
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000732115400001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17983
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cheng, Long
作者单位	1.Dublin City Univ, Sch Elect Engn, Dublin D09 V209 9, Ireland 2.Dublin City Univ, Sch Comp, Dublin D09 V209 9, Ireland 3.Dublin City Univ, Insight Ctr Data Analyt, Dublin D09 V209 9, Ireland 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 5.Wageningen Univ & Res, Informat Technol Grp, NL-6708 PB Wageningen, Netherlands 6.Dublin City Univ, SFI Insight Ctr Data Analyt, Dublin D09 V209 9, Ireland
推荐引用方式 GB/T 7714	Liu, Mingming,Cheng, Long,Gu, Yingqi,et al. MPC-CSAS: Multi-Party Computation for Real-Time Privacy-Preserving Speed Advisory Systems[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021:7.
APA	Liu, Mingming,Cheng, Long,Gu, Yingqi,Wang, Ying,Liu, Qingzhi,&O'Connor, Noel E..(2021).MPC-CSAS: Multi-Party Computation for Real-Time Privacy-Preserving Speed Advisory Systems.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,7.
MLA	Liu, Mingming,et al."MPC-CSAS: Multi-Party Computation for Real-Time Privacy-Preserving Speed Advisory Systems".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021):7.