Privacy-Preserving Driver Monitoring on the Edges: Transformer-Based Processing of Secret Shares from Video Streams

Modern vehicles increasingly rely on advanced driver monitoring systems (DMS) to ensure safety and enhance the driving experience. These systems assess driver status to prevent accidents caused by fatigue, inattentiveness, or intoxication. While some DMS applications process video data on vehicle, many rely on edge or cloud-based solutions, raising significant privacy concerns due to the storage of sensor data from vehicles. Existing approaches, such as de-identification and homomorphic encryption, either impose heavy computational overhead on vehicles or insufficiently address privacy. To overcome these limitations, we present the Privacy-preserving Driver Monitoring System (PDMS), a novel framework based on the additive secret sharing theory and privacy-preserving Transformer-based deep learning models. PDMS creates randomized secret shares from driver’s facial video data on vehicle, processes them independently through privacy-preserving Transformer models on edges, and securely aggregates partial results on vehicle, ensuring vehicles’ sensor data and final results remain protected. This approach reduces the computational load on the vehicle, enabling cost-effective and scalable DMS solutions that protect the privacy of the driver both in transit and in processing. Our contributions include the design and optimization of the PDMS system, incorporating privacy-preserving DNN layers that are capable of processing randomized secret shares. Furthermore, we present a practical system that utilizes a vision transformer (ViT)-based gaze estimation model, demonstrating the effectiveness of PDMS through comprehensive experiments.