Function Secret Sharing for Mixed-Mode and Fixed-Point Secure Computation
Elette Boyle, Nishanth Chandran, Niv Gilboa, Divya Gupta, Yuval Ishai, Nishant Kumar, Mayank Rathee
摘要
Boyle et al. (TCC 2019) proposed a new approach for secure computation in the preprocessing model building on function secret sharing (FSS), where a gate g is evaluated using an FSS scheme for the related offset family gr(x) = g(x + r). They further presented efficient FSS schemes based on any pseudorandom generator (PRG) for the offset families of several useful gates g that arise in "mixed-mode" secure computation. These include gates for zero test, integer comparison, ReLU, and spline functions. The FSS-based approach offers significant savings in online communication and round complexity compared to alternative techniques based on garbled circuits or secret sharing. In this work, we improve and extend the previous results of Boyle et al. by making the following three kinds of contributions:
-Improved Key Size. The preprocessing and storage costs of the FSS-based approach directly depend on the FSS key size. We improve the key size of previous constructions through two steps. First, we obtain roughly 4× reduction in key size for Distributed Comparison Function (DCF), i.e., FSS for the family of functions f < α,β (x) that output β if x < α and 0 otherwise. DCF serves as a central building block in the constructions of Boyle et al.. Second, we improve the number of DCF instances required for realizing useful gates g. For example, whereas previous FSS schemes for ReLU and m-piece spline required 2 and 2m DCF instances, respectively, ours require only a single instance of DCF in both cases. This improves the FSS key size by 6 -22× for commonly used gates such as ReLU and sigmoid.
-New Gates. We present the first PRG-based FSS schemes for arithmetic and logical shift gates, as well as for bit-decomposition where both the input and outputs are shared over Z2n . These gates are crucial for many applications related to fixed-point arithmetic and machine learning. -A Barrier. The above results enable a 2-round PRG-based secure evaluation of "multiply-thentruncate," a central operation in fixed-point arithmetic, by sequentially invoking FSS schemes for multiplication and shift. We identify a barrier to obtaining a 1-round implementation via a single FSS scheme, showing that this would require settling a major open problem in the area of FSS: namely, a PRG-based FSS for the class of bit-conjunction functions.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了最后一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper31
- SiRnn: A Math Library for Secure RNN InferenceDeevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta 等S&P 2021 · 被引用 154 次
- Waldo: A Private Time-Series Database from Function Secret SharingEmma Dauterman, Mayank Rathee, Raluca Ada Popa, Ion StoicaS&P 2022 · 被引用 91 次
- Correlated Pseudorandomness from Expand-Accumulate CodesElette Boyle, Geoffroy Couteau, Niv Gilboa, Yuval Ishai 等CRYPTO 2022 · 被引用 66 次
- Orca: FSS-based Secure Training and Inference with GPUsNeha Jawalkar, Kanav Gupta, Arkaprava Basu, Nishanth Chandran 等S&P 2024 · 被引用 58 次
- Structure-Aware Private Set Intersection, with Applications to Fuzzy MatchingGayathri Garimella, Mike Rosulek, Jaspal SinghCRYPTO 2022 · 被引用 34 次
它引用的顶会 Paper16
- SecureML: A System for Scalable Privacy-Preserving Machine LearningPayman Mohassel, Yupeng ZhangS&P 2017 · 被引用 2,107 次
- GAZELLE: A Low Latency Framework for Secure Neural Network InferenceChiraag Juvekar, Vinod Vaikuntanathan, Anantha P. ChandrakasanUSENIX Security 2018 · 被引用 1,075 次
- ABY3: A Mixed Protocol Framework for Machine LearningPayman Mohassel, Peter RindalCCS 2018 · 被引用 898 次
- Oblivious Neural Network Predictions via MiniONN TransformationsJian Liu, Mika Juuti, Yao Lu, N. AsokanCCS 2017 · 被引用 800 次
- High-Throughput Semi-Honest Secure Three-Party Computation with an Honest MajorityToshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof 等CCS 2016 · 被引用 463 次
相关 Paper
- Function Secret Sharing: Improvements and ExtensionsElette Boyle, Niv Gilboa, Yuval IshaiCCS 2016 · 被引用 404 次
- Distributed Function Secret Sharing and ApplicationsPengzhi Xing, Hongwei Li, Meng Hao, Hanxiao Chen 等NDSS 2025
- Homomorphic Secret Sharing: Optimizations and ApplicationsElette Boyle, Geoffroy Couteau, Niv Gilboa, Yuval Ishai 等CCS 2017 · 被引用 97 次
- Private Access Control for Function Secret SharingSacha Servan-Schreiber, Simon Beyzerov, Eli Yablon, Hyojae ParkS&P 2023
- Distributed Vector-OLE: Improved Constructions and ImplementationPhillipp Schoppmann, Adrià Gascón, Leonie Reichert, Mariana RaykovaCCS 2019 · 被引用 126 次
