PrivacySignal: Privacy-preserving traffic signal control for intelligent transportation system
Publication Type
Journal Article
Publication Date
2-2022
Abstract
A new trend of using deep reinforcement learning for traffic signal control has become a spotlight in the Intelligent Transportation System (ITS). However, the traditional intelligent traffic signal control system always collects and transmits vehicle information (e.g., vehicle location, speed, etc.) in the form of plaintext, which would result in the leakage of commuters' privacy and thus bring unnecessary troubles to users. In this paper, we propose a privacy-preserving traffic signal control for an intelligent transportation system (PrivacySignal). It relies on the existing road facilities to achieve the privacy of commuters, which guarantees the practicality of the system. Real-time decision-making and confidentiality of the system can be achieved simultaneously via the design of a series of secure and efficient interactive protocols, that are based on additive secret sharing, to perform the deep Q-network (DQN). Moreover, the security of PrivacySignal is testified, meanwhile, the system effectiveness, and the overall efficiency of PrivacySignal is demonstrated through theoretical analysis and simulation experiments. Compared with the existing privacy-preserving schemes of the intelligent traffic signal, PrivacySignal provides a general DQN based privacy-preserving traffic signal control strategy architecture with high efficiency and low-performance loss.
Keywords
Protocols, Real-time systems, Roads, Reinforcement learning, Privacy, Data privacy, Cryptography, Secure multiparty computation, privacy-preserving, deep reinforcement learning, intelligent transportation systems, intelligent traffic signal control
Discipline
Information Security
Research Areas
Cybersecurity
Publication
IEEE Transactions on Intelligent Transportation Systems
Volume
23
Issue
9
First Page
16290
Last Page
16303
ISSN
1524-9050
Identifier
10.1109/TITS.2022.3149600
Publisher
Institute of Electrical and Electronics Engineers
Citation
YING, Zuobin; CAO, Shuanglong; LIU, Ximeng; MA, Zhuo; MA, Jianfeng; and DENG, Robert H..
PrivacySignal: Privacy-preserving traffic signal control for intelligent transportation system. (2022). IEEE Transactions on Intelligent Transportation Systems. 23, (9), 16290-16303.
Available at: https://ink.library.smu.edu.sg/sis_research/7829
Additional URL
https://doi.org/10.1109/TITS.2022.3149600