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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Veröffentlicht in:Proceedings - IEEE International Parallel and Distributed Processing Symposium S. 629 - 640
Hauptverfasser: Fan, Xiaoyu, Chen, Kun, Wang, Guosai, Zhu, Xiaowei, He, Haoqing, Yong, Xie, Jia, Xiaofeng, Li, Yidong, Xu, Wei
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 03.06.2025
Schlagworte:
C++ languages
Codes
Distributed processing
Encoding
Engines
Multi-party computation
Parallel processing
Parallel programming
parallel programming paradigm
Scalability
secure multi-party computation
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
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
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  organization: Beijing Big Data Center
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  givenname: Wei
  surname: Xu
  fullname: Xu, Wei
  organization: Tsinghua University,IIIS
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Snippet Pair-then-Aggregate (PtA) introduces a programming paradigm and an automated parallel execution engine for large-scale secure multi-party (MPC) computations,...
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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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