ABSTRACT
Some of the most efficient protocols for Multi-Party Computation (MPC) follow a two-phase approach where correlated randomness, in particular Beaver triples, is generated in the offline phase and then used to speed up the online phase. Recently, more complex correlations have been introduced to optimize certain operations even further, such as matrix triples for matrix multiplications. In this paper, our goal is to improve the efficiency of the triple generation in general and in particular for classical field values as well as matrix operations. To this end, we modify the Overdrive LowGear protocol to remove the costly sacrificing step and therewith reduce the round complexity and the bandwidth. We extend the state-of-the-art MP-SPDZ implementation with our new protocols and show that the new offline phase outperforms state-of-the-art protocols for the generation of Beaver triples and matrix triples. For example, we save in bandwidth compared to Overdrive LowGear.
- Judit Bar-Ilan and Donald Beaver. 1989. Non-Cryptographic Fault-Tolerant Computing in Constant Number of Rounds of Interaction. In PODC 1989. ACM, 201–209.Google Scholar
- Carsten Baum, Daniele Cozzo, and Nigel P. Smart. 2020. Using TopGear in Overdrive: A More Efficient ZKPoK for SPDZ. In SAC 2019. Springer, 274–302.Google Scholar
- Donald Beaver. 1992. Efficient Multiparty Protocols Using Circuit Randomization. In CRYPTO ’91. Springer, 420–432.Google Scholar
- Michael Ben-Or, Shafi Goldwasser, and Avi Wigderson. 1988. Completeness Theorems for Non-Cryptographic Fault-Tolerant Distributed Computation (Extended Abstract). In STOC 1988. ACM, 1–10.Google ScholarDigital Library
- Christina Boura, Ilaria Chillotti, Nicolas Gama, Dimitar Jetchev, Stanislav Peceny, and Alexander Petric. 2018. High-Precision Privacy-Preserving Real-Valued Function Evaluation. In FC 2018. Springer, 183–202.Google ScholarDigital Library
- Elette Boyle, Geoffroy Couteau, Niv Gilboa, Yuval Ishai, Lisa Kohl, and Peter Scholl. 2019. Efficient Pseudorandom Correlation Generators: Silent OT Extension and More. In CRYPTO 2019. Springer, 489–518.Google ScholarDigital Library
- Elette Boyle, Geoffroy Couteau, Niv Gilboa, Yuval Ishai, Lisa Kohl, and Peter Scholl. 2022. Efficient Pseudorandom Correlation Generators from Ring-LPN. Cryptology ePrint Archive, Paper 2022/1035. https://eprint.iacr.org/2022/1035Google Scholar
- Zvika Brakerski. 2012. Fully Homomorphic Encryption without Modulus Switching from Classical GapSVP. In CRYPTO 2012. Springer, 868–886.Google ScholarDigital Library
- Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan. 2012. (Leveled) Fully Homomorphic Encryption Without Bootstrapping. In ITCS 2012. ACM, 309–325.Google ScholarDigital Library
- Ran Canetti. 2001. Universally Composable Security: A New Paradigm for Cryptographic Protocols. In FOCS 2001. IEEE, 136–145.Google Scholar
- Hao Chen, Miran Kim, Ilya P. Razenshteyn, Dragos Rotaru, Yongsoo Song, and Sameer Wagh. 2020. Maliciously Secure Matrix Multiplication with Applications to Private Deep Learning. In ASIACRYPT 2020. Springer, 31–59. Implementation: https://github.com/snwagh/ponytail-public/.Google ScholarDigital Library
- Carbyne Stack Contributors. 2021. Carbyne Stack: Open Source Cloud Native Secure Multiparty Computation. Available at https://carbynestack.io/.Google Scholar
- Ronald Cramer and Ivan Damgård. 2001. Secure Distributed Linear Algebra in a Constant Number of Rounds. In CRYPTO 2001. Springer, 119–136.Google ScholarCross Ref
- Ronald Cramer, Ivan Damgård, Daniel Escudero, Peter Scholl, and Chaoping Xing. 2018. SPD : Efficient MPC Mod for Dishonest Majority. In CRYPTO 2018. Springer, 769–798.Google Scholar
- Morten Dahl. 2017. Cryptography and ML. https://mortendahl.github.ioGoogle Scholar
- Anders Dalskov, Daniel Escudero, and Marcel Keller. 2020. Secure Evaluation of Quantized Neural Networks. PETS 2020, 4 (2020), 355–375.Google Scholar
- Ivan Damgård, Martin Geisler, and Mikkel Kroigard. 2008. Homomorphic Encryption and Secure Comparison. Int. J. Appl. Cryptol. 1, 1 (2008), 22–31.Google ScholarDigital Library
- Ivan Damgård, Marcel Keller, Enrique Larraia, Valerio Pastro, Peter Scholl, and Nigel P. Smart. 2013. Practical Covertly Secure MPC for Dishonest Majority – Or: Breaking the SPDZ Limits. In ESORICS 2013. Springer, 1–18.Google Scholar
- Ivan Damgård, Valerio Pastro, Nigel Smart, and Sarah Zakarias. 2012. Multiparty Computation from Somewhat Homomorphic Encryption. In CRYPTO 2012. Springer, 643–662.Google ScholarDigital Library
- Ivan Damgård, Daniel Escudero, Tore Frederiksen, Marcel Keller, Peter Scholl, and Nikolaj Volgushev. 2019. New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning. In SP 2019. 1102–1120.Google ScholarCross Ref
- Junfeng Fan and Frederik Vercauteren. 2012. Somewhat Practical Fully Homomorphic Encryption. IACR Cryp. ePrint Arch. (2012), 144. https://ia.cr/2012/144Google Scholar
- Craig Gentry, Shai Halevi, and Nigel P. Smart. 2012. Fully Homomorphic Encryption with Polylog Overhead. In EUROCRYPT 2012. Springer, 465–482.Google ScholarDigital Library
- Craig Gentry, Shai Halevi, and Nigel P. Smart. 2012. Homomorphic Evaluation of the AES Circuit. In CRYPTO 2012. Springer, 850–867.Google ScholarDigital Library
- Oded Goldreich, Silvio Micali, and Avi Wigderson. 1987. How to Play any Mental Game or A Completeness Theorem for Protocols with Honest Majority. In STOC 1987. ACM, 218–229.Google ScholarDigital Library
- Shai Halevi and Victor Shoup. 2014. Algorithms in HElib. In CRYPTO 2014. Springer, 554–571.Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR 2016. IEEE, 770–778.Google Scholar
- Zhicong Huang, Wen-jie Lu, Cheng Hong, and Jiansheng Ding. 2022. Cheetah: Lean and Fast Secure Two-Party Deep Neural Network Inference. IACR Cryptol. ePrint Arch. (2022), 207. https://eprint.iacr.org/2022/207Google Scholar
- Xiaoqian Jiang, Miran Kim, Kristin E. Lauter, and Yongsoo Song. 2018. Secure Outsourced Matrix Computation and Application to Neural Networks. In CCS 2018. ACM, 1209–1222.Google Scholar
- Marcel Keller. 2020. MP-SPDZ: A Versatile Framework for Multi-Party Computation. In CCS ’20: 2020 ACM, Virtual Event. ACM, 1575–1590.Google Scholar
- Marcel Keller, Emmanuela Orsini, and Peter Scholl. 2016. MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious Transfer. In CCS. ACM, 830–842.Google ScholarDigital Library
- Marcel Keller, Valerio Pastro, and Dragos Rotaru. 2018. Overdrive: Making SPDZ Great Again. In EUROCRYPT 2018. Springer, 158–189.Google Scholar
- Marcel Keller and Ke Sun. 2022. Secure Quantized Training for Deep Learning. In ICML 2022. PMLR, 10912–10938.Google Scholar
- Toomas Krips, Ralf Küsters, Pascal Reisert, Marc Rivinius, and Johannes Schäufele. 2022. Overdrive 2.0: Implementation. Available on request.Google Scholar
- F. Thomson Leighton. 1991. Introduction to parallel algorithms and architectures: Arrays, trees, hypercubes. Elsevier.Google Scholar
- Vadim Lyubashevsky, Chris Peikert, and Oded Regev. 2013. A Toolkit for Ring-LWE Cryptography. In EUROCRYPT 2013. Springer, 35–54.Google ScholarCross Ref
- Payman Mohassel and Peter Rindal. 2018. ABY3: A Mixed Protocol Framework for Machine Learning. In CCS ’18. ACM, 35–52.Google Scholar
- Payman Mohassel and Yupeng Zhang. 2017. SecureML: A System for Scalable Privacy-Preserving Machine Learning. In SP 2017. IEEE Computer Society, 19–38.Google Scholar
- Emmanuela Orsini. 2021. Efficient, Actively Secure MPC with a Dishonest Majority: A Survey. In Arithmetic of Finite Fields. Springer, 42–71.Google Scholar
- Emmanuela Orsini, Nigel P. Smart, and Frederik Vercauteren. 2020. Overdrive2k: Efficient Secure MPC over from Somewhat Homomorphic Encryption. In CT-RSA 2020. Springer, 254–283.Google Scholar
- Valerio Pastro. 2013. Zero-Knowledge Protocols and Multiparty Computation. Ph. D. Dissertation. Aarhus University. Advisor(s) Damgård, Ivan.Google Scholar
- Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, and Rahul Sharma. 2020. CrypTFlow2: Practical 2-Party Secure Inference. In CCS 2020. ACM, 325–342.Google ScholarDigital Library
- Dragos Rotaru, Nigel P. Smart, Titouan Tanguy, Frederik Vercauteren, and Tim Wood. 2022. Actively Secure Setup for SPDZ. J. Cryptol. 35, 1 (2022), 5.Google ScholarDigital Library
- Jinhyun So, Başak Güler, and A. Salman Avestimehr. 2021. CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning. J. Sel. Areas Inf. Theory 2, 1 (2021), 441–451.Google ScholarCross Ref
- Sameer Wagh, Divya Gupta, and Nishanth Chandran. 2019. SecureNN: 3-Party Secure Computation for Neural Network Training. PETS 2019, 3 (2019), 26–49.Google ScholarCross Ref
- Sameer Wagh, Shruti Tople, Fabrice Benhamouda, Eyal Kushilevitz, Prateek Mittal, and Tal Rabin. 2021. Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning. PETS 2021, 1 (2021), 188–208.Google Scholar
- Wenting Zheng, Raluca A. Popa, Joseph Gonzalez, and Ion Stoica. 2019. Helen: Maliciously Secure Coopetitive Learning for Linear Models. SP 2019, 724–738.Google Scholar
Index Terms
- Overdrive LowGear 2.0: Reduced-Bandwidth MPC without Sacrifice
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