Pair-Then-Aggregate: Simplified and Efficient Parallel Programming Paradigm for Secure Multi-Party Computation

Xiaoyu Fan, Kun Chen, Guosai Wang, Xiaowei Zhu, Haoqing He, Xie Yong, Xiaofeng Jia, Yidong Li, Wei Xu

Abstract: Pair-then-Aggregate (PtA) introduces a programming paradigm and an automated parallel execution engine for large-scale secure multi-party (MPC) computations, drawing inspiration from the widely-used yet not explicitly defined Table-Generation-and-Look-up (TGL) pattern in privacy-preserving algorithm design. PtA offers an easy-to-use API and a versatile execution engine that harnesses various levels of parallelism and adapts to different MPC deployments, algorithms, and input sizes. Evaluations on a real-world MPC platform demonstrate significant enhancements in scalability, adaptability, and ease of programming. PtA can process one billion input elements with 3-23 lines of C++ code in 5-74 seconds. It outperforms state-of-the-art implementations in 91.4 % of 35 test cases, achieving up to a $12.4 \times$ speedup with much less coding effort. 11Our code is provided in https://github.com/Fannxy/Pair-then-Aggregate