MPC-PAT: A Pipeline Architecture for Beaver Triple Generation in Secure Multi-party Computation
Xiaolin Li, Wei Yan, Hongwei Liu, Yong Zhang, Qinfen Hao, Liu Yong, Ninghui Sun · 2024
Secure Multi-Party Computation (MPC) is proposed to protect the data privacy from a group of parties, enabling collaborative computation of correct results for target functions. SPDZ, a set of mature MPC protocols widely used in machine learning and other scenarios, requires a significant number of Beaver triples for secure multiplications among parties. Given no Trusted Third Party (TTP) participated, the generation time constitutes over 92% of the total running time. This paper introduces MPC-PAT, a high-performance pipeline architecture designed for efficient Beaver triple generation. MPC-PAT accelerates random number generation, hash function, and modular multiplication(MM) in two finite fields. The evaluation results from its FPGA implementation demonstrate 99× speed-up for basic operations and 136× speed-up for various convolutional networks compared to the existing SPDZ works.