Sharing Transformation and Dishonest Majority MPC with Packed Secret Sharing
Vipul Goyal, Antigoni Polychroniadou, Yifan Song
摘要
In the last few years, the efficiency of secure multi-party computation (MPC) in the dishonest majority setting has increased by several orders of magnitudes starting with the SPDZ protocol family which offers a speedy information-theoretic online phase in the prepossessing model. However, state-of-the-art n-party MPC protocols in the dishonest majority setting incur online communication complexity per multiplication gate which is linear in the number of parties, i.e. O(n), per gate across all parties. In this work, we construct the first MPC protocols in the preprocessing model for dishonest majority with sublinear communication complexity per gate in the number of parties n. To achieve our results, we extend the use of packed secret sharing to the dishonest majority setting. For a constant fraction of corrupted parties (i.e. if 99 percent of the parties are corrupt), we can achieve a communication complexity of O(1) field elements per multiplication gate across all parties.
At the crux of our techniques lies a new technique called sharing transformation. The sharing transformation technique allows us to transform shares under one type of linear secret sharing scheme into another, and even perform arbitrary linear maps on the secrets of (packed) secret sharing schemes with optimal communication complexity. This technique can be of independent interest since transferring shares from one type of scheme into another (e.g., for degree reduction) is ubiquitous in MPC. Furthermore, we introduce what we call sparsely packed Shamir sharing which allows us to address the issue of network routing efficiently, and packed Beaver triples which is an extension of the widely used technique of Beaver triples for packed secret sharing (for dishonest majority).
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引用它的顶会 Paper4
- TurboPack: Honest Majority MPC with Constant Online CommunicationDaniel Escudero, Vipul Goyal, Antigoni Polychroniadou, Yifan SongCCS 2022 · 被引用 25 次
- MD-ML: Super Fast Privacy-Preserving Machine Learning for Malicious Security with a Dishonest MajorityBoshi Yuan, Shixuan Yang, Yongxiang Zhang, Ning Ding 等USENIX Security 2024 · 被引用 22 次
- zkSaaS: Zero-Knowledge SNARKs as a ServiceSanjam Garg, Aarushi Goel, Abhishek Jain, Guru-Vamsi Policharla 等USENIX Security 2023
- Scalable Collaborative zk-SNARK and Its Application to Fully Distributed Proof DelegationXuanming Liu, Zhelei Zhou, Yinghao Wang, Yanxin Pang 等USENIX Security 2025
它引用的顶会 Paper7
- ATLAS: Efficient and Scalable MPC in the Honest Majority SettingVipul Goyal, Hanjun Li, Rafail Ostrovsky, Antigoni Polychroniadou 等CRYPTO 2021 · 被引用 56 次
- Unconditional Communication-Efficient MPC via Hall's Marriage TheoremVipul Goyal, Antigoni Polychroniadou, Yifan SongCRYPTO 2021 · 被引用 35 次
- Asymptotically-Good Arithmetic Secret Sharing over with Strong Multiplication and Its Applications to Efficient MPCRonald Cramer, Matthieu Rambaud, Chaoping XingCRYPTO 2021 · 被引用 26 次
- Sublinear GMW-Style Compiler for MPC with PreprocessingElette Boyle, Niv Gilboa, Yuval Ishai, Ariel NofCRYPTO 2021 · 被引用 26 次
- The More the Merrier: Reducing the Cost of Large Scale MPCS. Dov Gordon, Daniel Starin, Arkady YerukhimovichEUROCRYPT 2021 · 被引用 25 次
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