PentaGOD: Stepping beyond Traditional GOD with Five Parties
Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, Bhavish Raj Gopal
Abstract
Secure multiparty computation (MPC) is increasingly being used to address privacy issues in various applications. The recent work of Alon et al. (CRYPTO'20) identified the shortcomings of traditional MPC and defined a Friends-and-Foes (FaF) security notion to address the same. We showcase the need for FaF security in real-world applications such as dark pools. This subsequently necessitates designing concretely efficient FaF-secure protocols. Towards this, keeping efficiency at the center stage, we design ring-based FaF-secure MPC protocols in the small-party honest-majority setting. Specifically, we provide (1,1)-FaF secure 5 party computation protocols (5PC) that consider one malicious and one semi-honest corruption and constitutes the optimal setting for attaining honest-majority. At the heart of it lies the multiplication protocol that requires a single round of communication with 8 ring elements (amortized). To facilitate having FaF-secure variants for several applications, we design a variety of building blocks optimized for our FaF setting. The practicality of the designed (1,1)-FaF secure 5PC framework is showcased by benchmarking dark pools. In the process, we also improve the efficiency and security of the dark pool protocols over the existing traditionally secure ones. This improvement is witnessed as a gain of up to 62x in throughput compared to the existing ones. Finally, to demonstrate the versatility of our framework, we also benchmark popular deep neural networks.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 93091dc1-26c5-4b39-b4a4-382cef9b39bfCited by top-tier papers2
- "Get in Researchers; We're Measuring Reproducibility": A Reproducibility Study of Machine Learning Papers in Tier 1 Security ConferencesDaniel Olszewski, Allison Lu, Carson Stillman, Kevin Warren et al.CCS 2023 · 19 citations
- Sublinear Distributed Product Checks on Replicated Secret-Shared Data over Z2k Without Ring ExtensionsYun Li, Daniel Escudero, Yufei Duan, Zhicong Huang et al.CCS 2024 · 1 citation
Builds on13
- SecureML: A System for Scalable Privacy-Preserving Machine LearningPayman Mohassel, Yupeng ZhangS&P 2017 · 2,107 citations
- ABY3: A Mixed Protocol Framework for Machine LearningPayman Mohassel, Peter RindalCCS 2018 · 898 citations
- High-Throughput Semi-Honest Secure Three-Party Computation with an Honest MajorityToshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof et al.CCS 2016 · 463 citations
- SWIFT: Super-fast and Robust Privacy-Preserving Machine LearningNishat Koti, Mahak Pancholi, Arpita Patra, Ajith SureshUSENIX Security 2021 · 184 citations
- Optimized Honest-Majority MPC for Malicious Adversaries - Breaking the 1 Billion-Gate Per Second BarrierToshinori Araki, Assi Barak, Jun Furukawa, Tamar Lichter et al.S&P 2017 · 137 citations
Related papers
- Asterisk: Super-fast MPC with a FriendBanashri Karmakar, Nishat Koti, Arpita Patra, Sikhar Patranabis et al.S&P 2024 · 17 citations
- ABY2.0: Improved Mixed-Protocol Secure Two-Party ComputationArpita Patra, Thomas Schneider, Ajith Suresh, Hossein YalameUSENIX Security 2021 · 307 citations
- Practical Fully Secure Three-Party Computation via Sublinear Distributed Zero-Knowledge ProofsElette Boyle, Niv Gilboa, Yuval Ishai, Ariel NofCCS 2019 · 71 citations
- MPC with Friends and FoesBar Alon, Eran Omri, Anat Paskin-CherniavskyCRYPTO 2020 · 15 citations
- Fast Secure Computation for Small Population over the InternetMegha Byali, Arun Joseph, Arpita Patra, Divya RaviCCS 2018 · 26 citations
