PrivacySignal: Privacy-preserving traffic signal control for intelligent transportation system

• Main Authors: YING, Zuobin, CAO, Shuanglong, LIU, Ximeng, MA, Zhuo, MA, Jianfeng, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: 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; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/7829
• Institution: Singapore Management University

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.