SoK: General Purpose Compilers for Secure Multi-Party Computation
Marcella Hastings, Brett Hemenway, Daniel Noble, Steve Zdancewic
摘要
Secure multi-party computation (MPC) allows a group of mutually distrustful parties to compute a joint function on their inputs without revealing any information beyond the result of the computation. This type of computation is extremely powerful and has wide-ranging applications in academia, industry, and government. Protocols for secure computation have existed for decades, but only recently have general-purpose compilers for executing MPC on arbitrary functions been developed. These projects rapidly improved the state of the art, and began to make MPC accessible to non-expert users. However, the field is changing so rapidly that it is difficult even for experts to keep track of the varied capabilities of modern frameworks. In this work, we survey general-purpose compilers for secure multi-party computation. These tools provide high-level abstractions to describe arbitrary functions and execute secure computation protocols. We consider eleven systems: EMP-toolkit, Obliv-C, ObliVM, TinyGarble, SCALE-MAMBA (formerly SPDZ), Wysteria, Sharemind, PICCO, ABY, Frigate and CBMC-GC. We evaluate these systems on a range of criteria, including language expressibility, capabilities of the cryptographic back-end, and accessibility to developers. We advocate for improved documentation of MPC frameworks, standardization within the community, and make recommendations for future directions in compiler development. Installing and running these systems can be challenging, and for each system, we also provide a complete virtual environment (Docker container) with all the necessary dependencies to run the compiler and our example programs.
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引用它的顶会 Paper32
- CrypTen: Secure Multi-Party Computation Meets Machine LearningBrian Knott, Shobha Venkataraman, Awni Y. Hannun, Shubho Sengupta 等NeurIPS 2021 · 被引用 573 次
- SoK: Computer-Aided CryptographyManuel Barbosa, Gilles Barthe, Karthik Bhargavan, Bruno Blanchet 等S&P 2021 · 被引用 169 次
- SoK: Fully Homomorphic Encryption CompilersAlexander Viand, Patrick Jattke, Anwar HithnawiS&P 2021 · 被引用 117 次
- EVA: an encrypted vector arithmetic language and compiler for efficient homomorphic computationRoshan Dathathri, Blagovesta Kostova, Olli Saarikivi, Wei Dai 等PLDI 2020 · 被引用 117 次
- Efficient and Secure Multiparty Computation from Fixed-Key Block CiphersChun Guo, Jonathan Katz, Xiao Wang, Yu YuS&P 2020 · 被引用 96 次
它引用的顶会 Paper10
- ABY3: A Mixed Protocol Framework for Machine LearningPayman Mohassel, Peter RindalCCS 2018 · 被引用 898 次
- Scaling ORAM for Secure ComputationJack Doerner, Abhi ShelatCCS 2017 · 被引用 221 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
- Revisiting Square-Root ORAM: Efficient Random Access in Multi-party ComputationSamee Zahur, Xiao Wang, Mariana Raykova, Adrià Gascón 等S&P 2016 · 被引用 124 次
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