Privacy-preserving Intelligent Traffic Light Control

A new trend of using deep reinforcement learning for traffic light control has become a spotlight in the Intelligent Transportation System (ITS). Meanwhile, some promising accomplishments have also been made. 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. Privacy leaks cause unnecessary trouble for users. For example, the personal interests and health information of the commuter can be inferred based on the driving route of the vehicle, or receive harassment advertisements for surrounding services, etc. In this paper, we propose a privacy-preserving intelligent traffic light control (PTLC) system with Deep Q-network. To achieve practicality and confidentiality simultaneously, a series of secure and efficient interactive protocols is designed depending on additive secret sharing to perform the DQN. Moreover, the security of PTLC is testified, meanwhile, the system effectiveness, and the overall efficiency of PTLC is demonstrated through theoretical analysis and simulation experiments.