PPMLAC
Xing Zhou, Zhilei Xu, Cong Wang, Mingyu Gao
Abstract
Privacy issue is a main concern restricting data sharing and cross-organization collaborations. While Privacy-Preserving Machine Learning techniques such as Multi-Party Computations (MPC), Homomorphic Encryption, and Federated Learning are proposed to solve this problem, no solution exists with both strong security and high performance to run large-scale, complex machine learning models. This paper presents PPMLAC, a novel chipset architecture to accelerate MPC, which combines MPC's strong security and hardware's high performance, eliminates the communication bottleneck from MPC, and achieves several orders of magnitudes speed up over software-based MPC. It is carefully designed to only rely on a minimum set of simple hardware components in the trusted domain, thus is robust against side-channel attacks and malicious adversaries. Our FPGA prototype can run mainstream large-scale ML models like ResNet in near real-time under a practical network environment with non-negligible latency, which is impossible for existing MPC solutions.