MPC-Friendly Commitments for Publicly Verifiable Covert Security
Nitin Agrawal, James Bell, Adrià Gascón, Matt J. Kusner
摘要
We address the problem of efficiently verifying a commitment in a two-party computation. This addresses the scenario where a party P1 commits to a value x to be used in a subsequent secure computation with another party P2 that wants to receive assurance that P1 did not cheat, i.e. that x was indeed the value inputted into the secure computation. Our constructions operate in the publicly verifiable covert (PVC) security model, which is a relaxation of the malicious model of MPC, appropriate in settings where P1 faces a reputational harm if caught cheating. We introduce the notion of PVC commitment scheme and indexed hash functions to build commitment schemes tailored to the PVC framework, and propose constructions for both arithmetic and Boolean circuits that result in very efficient circuits. From a practical standpoint, our constructions for Boolean circuits are 60x faster to evaluate securely, and use 36x less communication than baseline methods based on hashing. Moreover, we show that our constructions are tight in terms of required non-linear operations, by proving lower bounds on the nonlinear gate count of commitment verification circuits. Finally, we present a technique to amplify the security properties our constructions that allows to efficiently recover malicious guarantees with statistical security.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,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 Paper3
- Holding Secrets Accountable: Auditing Privacy-Preserving Machine LearningHidde Lycklama, Alexander Viand, Nicolas Küchler, Christian Knabenhans 等USENIX Security 2024 · 被引用 11 次
- An Auditing Test to Detect Behavioral Shift in Language ModelsLeo Richter, Xuanli He, Pasquale Minervini, Matt J. KusnerICLR 2025
- DiStefano: Decentralized Infrastructure for Sharing Trusted Encrypted Facts and Nothing MoreSofía Celi, Alex Davidson, Hamed Haddadi, Gonçalo Pestana 等NDSS 2025
它引用的顶会 Paper5
- 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
- QUOTIENT: Two-Party Secure Neural Network Training and PredictionNitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià GascónCCS 2019 · 被引用 241 次
- Global-Scale Secure Multiparty ComputationXiao Wang, Samuel Ranellucci, Jonathan KatzCCS 2017 · 被引用 220 次
- Authenticated Garbling and Efficient Maliciously Secure Two-Party ComputationXiao Wang, Samuel Ranellucci, Jonathan KatzCCS 2017 · 被引用 212 次
相关 Paper
- Black-Box Transformations from Passive to Covert Security with Public VerifiabilityIvan Damgård, Claudio Orlandi, Mark SimkinCRYPTO 2020 · 被引用 14 次
- Generic Compiler for Publicly Verifiable Covert Multi-Party ComputationSebastian Faust, Carmit Hazay, David Kretzler, Benjamin SchlosserEUROCRYPT 2021 · 被引用 14 次
- Publicly Accountable Robust Multi-Party ComputationMarc Rivinius, Pascal Reisert, Daniel Rausch, Ralf KüstersS&P 2022 · 被引用 24 次
- MASCOT: Faster Malicious Arithmetic Secure Computation with Oblivious TransferMarcel Keller, Emmanuela Orsini, Peter SchollCCS 2016 · 被引用 487 次
- Practical Fully Secure Three-Party Computation via Sublinear Distributed Zero-Knowledge ProofsElette Boyle, Niv Gilboa, Yuval Ishai, Ariel NofCCS 2019 · 被引用 71 次
