EPAuto: Efficient Privacy-Preserving Machine Learning on AI-powered Autonomous Driving Systems using Multi-Party Computation

AI-powered autonomous driving systems are being increasingly adopted by automobile manufacturers, while training autonomous driving models using machine learning technique requires vast amounts of driving data. Manufacturers tend to collaborate through ecosystem partnerships to jointly advance the development of autonomous driving algorithms such as Huawei has established partnerships with multiple collaborators sharing intelligent automotive solutions. However, concerns about data privacy naturally arise among automobile manufacturers. This paper proposes a privacy-preserving framework, EPAuto, for collaborative machine learning among automobile manufacturers. EPAuto leverages secure multi-party computation (MPC) to enable efficient privacy-preserving machine learning (PPML). We propose our improved bit-wise comparison and most significant bit (MSB) extraction protocols, leveraging the random permutation matrix as the core technique. Additionally, we design fundamental building blocks for privacy-preserving machine learning (PPML), including DReLU, ReLU, and Maxpool protocols. The security of EPAuto is formally proven under the static semi-honest adversary model in the dishonest-majority setting. We implement our protocols within 2 to 120 parties in both LAN and WAN environments. Our results demonstrate nanosecond-level online response over LAN in multi-party settings, and our PPML implementations achieve up to a 907× performance improvement compared to state-of-the-art works in the same setting.