Pair-Then-Aggregate: Simplified and Efficient Parallel Programming Paradigm for Secure Multi-Party Computation - Fan, Xiaoyu; Chen, Kun; Wang, Guosai; Zhu, Xiaowei; He, Haoqing; Yong, Xie; Jia, Xiaofeng; Li, Yidong; Xu, Wei (Proceedings - IEEE International Parallel and Distributed Processing Symposium, 2025)
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 × speedup with much less coding effort. 1 1 Our code is provided in https://github.com/Fannxy/Pair-then-Aggregate