Pair-Then-Aggregate: Simplified and Efficient Parallel Programming Paradigm for Secure Multi-Party Computation
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...
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| Vydané v: | Proceedings - IEEE International Parallel and Distributed Processing Symposium s. 629 - 640 |
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| Hlavní autori: | , , , , , , , , |
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| Jazyk: | English |
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IEEE
03.06.2025
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| ISSN: | 1530-2075 |
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| 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. 1 1 Our code is provided in https://github.com/Fannxy/Pair-then-Aggregate |
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| AbstractList | 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. 1 1 Our code is provided in https://github.com/Fannxy/Pair-then-Aggregate |
| Author | Zhu, Xiaowei Jia, Xiaofeng Li, Yidong Wang, Guosai Yong, Xie He, Haoqing Chen, Kun Fan, Xiaoyu Xu, Wei |
| Author_xml | – sequence: 1 givenname: Xiaoyu surname: Fan fullname: Fan, Xiaoyu organization: Tsinghua University,IIIS – sequence: 2 givenname: Kun surname: Chen fullname: Chen, Kun organization: Ant Group – sequence: 3 givenname: Guosai surname: Wang fullname: Wang, Guosai organization: Tsingjiao Information Technology Co. Ltd – sequence: 4 givenname: Xiaowei surname: Zhu fullname: Zhu, Xiaowei organization: Ant Group – sequence: 5 givenname: Haoqing surname: He fullname: He, Haoqing organization: Tsinghua University,IIIS – sequence: 6 givenname: Xie surname: Yong fullname: Yong, Xie organization: Qinghai University,Department of Computer Technology and Application – sequence: 7 givenname: Xiaofeng surname: Jia fullname: Jia, Xiaofeng organization: Beijing Jiaotong University – sequence: 8 givenname: Yidong surname: Li fullname: Li, Yidong organization: Beijing Big Data Center – sequence: 9 givenname: Wei surname: Xu fullname: Xu, Wei organization: Tsinghua University,IIIS |
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| SubjectTerms | C++ languages Codes Distributed processing Encoding Engines Multi-party computation Parallel processing Parallel programming parallel programming paradigm Scalability secure multi-party computation |
| Title | Pair-Then-Aggregate: Simplified and Efficient Parallel Programming Paradigm for Secure Multi-Party Computation |
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