FLUTE: Fast and Secure Lookup Table Evaluations
Andreas Brüggemann, Robin Hundt, Thomas Schneider, Ajith Suresh, Hossein Yalame
摘要
The concept of using Lookup Tables (LUTs) instead of Boolean circuits is well-known and been widely applied in a variety of applications, including FPGAs, image processing, and database management systems. In cryptography, using such LUTs instead of conventional gates like AND and XOR results in more compact circuits and has been shown to substantially improve online performance when evaluated with secure multi-party computation. Several recent works on secure floating-point computations and privacy-preserving machine learning inference rely heavily on existing LUT techniques. However, they suffer from either large overhead in the setup phase or subpar online performance.We propose FLUTE, a novel protocol for secure LUT evaluation with good setup and online performance. In a two-party setting, we show that FLUTE matches or even outperforms the online performance of all prior approaches, while being competitive in terms of overall performance with the best prior LUT protocols. In addition, we provide an open-source implementation of FLUTE written in the Rust programming language, and implementations of the Boolean secure two-party computation protocols of ABY2.0 and silent OT. We find that FLUTE outperforms the state of the art by two orders of magnitude in the online phase while retaining similar overall communication.
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引用它的顶会 Paper16
- Don't Eject the Impostor: Fast Three-Party Computation With a Known CheaterAndreas Brüggemann, Oliver Schick, Thomas Schneider, Ajith Suresh 等S&P 2024 · 被引用 13 次
- Garbled Circuit Lookup Tables with Logarithmic Number of CiphertextsDavid Heath, Vladimir Kolesnikov, Lucien K. L. NgEUROCRYPT 2024 · 被引用 11 次
- Compressing Unit-Vector Correlations via Sparse Pseudorandom GeneratorsAmit Agarwal, Elette Boyle, Niv Gilboa, Yuval Ishai 等CRYPTO 2024 · 被引用 6 次
- Ironman: Accelerating Oblivious Transfer Extension for Privacy-Preserving AI with Near-Memory ProcessingChenqi Lin, Kang Yang, Tianshi Xu, Ling Liang 等MICRO 2025 · 被引用 4 次
- Accelerating Multiparty Noise Generation Using LookupsFredrik Meisingseth, Christian Rechberger, Fabian SchmidCCS 2026 · 被引用 3 次
它引用的顶会 Paper21
- Practical Secure Aggregation for Privacy-Preserving Machine LearningKallista A. Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone 等CCS 2017 · 被引用 3,936 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
- XONN: XNOR-based Oblivious Deep Neural Network InferenceM. Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine 等USENIX Security 2019 · 被引用 314 次
- ABY2.0: Improved Mixed-Protocol Secure Two-Party ComputationArpita Patra, Thomas Schneider, Ajith Suresh, Hossein YalameUSENIX Security 2021 · 被引用 307 次
- CrypTFlow2: Practical 2-Party Secure InferenceDeevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran 等CCS 2020 · 被引用 294 次
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