SEPPDL: A Secure and Efficient Privacy-Preserving Deep Learning Inference Framework for Autonomous Driving

The autonomous driving system necessitates using privacy-preserving deep learning (PPDL) technologies as the safety assurance for its extensive application. However, existing PPDL solutions depend on intricate protocol designs for robust security. Although leveraging advanced dedicated hardware platforms can significantly improve inference efficiency, the PPDL frameworks that make the best use of hardware platform computility are scarce. Thus, balancing efficiency and security in PPDL remains an open question. This study presents SEPPDL, a secure tripartite inference framework for deep learning based on secret-sharing to balance privacy security and computational efficiency. We reduce the communication and calculation time by designing a deep learning quantization representation scheme, two new computational protocols, and a computation library that utilizes the integer computation units of the GPU. The experimental results show that compared with state-of-the-art PPDL frameworks, the SEPPDL framework reduces the communication and computation delay in the model inference to 1/2 and 1/3 of the existing optimal frameworks while maintaining the accuracy of the model inference. Meanwhile, the SEPPDL framework achieves a 10-fold performance improvement in a lightweight model. As the model scale increases, the performance of the SEPPDL-based model even achieves an 86-fold improvement compared to VGG16.