NFGen: Automatic Non-linear Function Evaluation Code Generator for General-purpose MPC Platforms
Xiaoyu Fan, Kun Chen, Guosai Wang, Mingchun Zhuang, Yi Li, Wei Xu
摘要
Due to the absence of a library for non-linear function evaluation, so-called general-purpose secure multi-party computation (MPC) are not as "general" as MPC programmers expect. Prior arts either naively reuse plaintext methods, resulting in suboptimal performance and even incorrect results, or handcraft ad hoc approximations for specific functions or platforms. We propose a general technique, NFGen 1 , that utilizes pre-computed discrete piecewise polynomials to accurately approximate generic functions using fixed-point numbers. We implement it using a performance-prediction-based code generator to support different platforms. Conducting extensive evaluations of 23 non-linear functions against six MPC protocols on two platforms, we demonstrate significant performance, accuracy, and generality improvements over existing methods. CCS CONCEPTS • Security and privacy → Security services.
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引用它的顶会 Paper5
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- Rushing at SPDZ: On the Practical Security of Malicious MPC ImplementationsAlexander Kyster, Frederik Huss Nielsen, Sabine Oechsner, Peter SchollS&P 2025
- RoundRole: Unlocking the Efficiency of Multi-party Computation with Bandwidth-aware ExecutionXiaoyu Fan, Kun Chen, Jiping Yu, Xin Liu 等NDSS 2026
- SLOTHE : Lazy Approximation of Non-Arithmetic Neural Network Functions over Encrypted DataKevin Nam, Youyeon Joo, Seungjin Ha, Yunheung PaekUSENIX Security 2025
它引用的顶会 Paper10
- SecureML: A System for Scalable Privacy-Preserving Machine LearningPayman Mohassel, Yupeng ZhangS&P 2017 · 被引用 2,107 次
- ABY3: A Mixed Protocol Framework for Machine LearningPayman Mohassel, Peter RindalCCS 2018 · 被引用 898 次
- CrypTen: Secure Multi-Party Computation Meets Machine LearningBrian Knott, Shobha Venkataraman, Awni Y. Hannun, Shubho Sengupta 等NeurIPS 2021 · 被引用 573 次
- CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUSijun Tan, Brian Knott, Yuan Tian, David J. WuS&P 2021 · 被引用 241 次
- New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine LearningIvan Damgård, Daniel Escudero, Tore Kasper Frederiksen, Marcel Keller 等S&P 2019 · 被引用 182 次
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