SoK: General Purpose Compilers for Secure Multi-Party Computation
By Marcella Hastings, Brett Hemenway, Daniel Noble, Steve Zdancewic. 2019
186 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Awni Y. Hannun
- L. V. D. van der Maaten
- Shobha Venkataraman
- Shubho Sengupta
- Brian Knott
- Mark Ibrahim
- Marcel Keller
- Ishan Misra
In Computational Theory and Mathematics
- Awni Y. Hannun
- L. V. D. van der Maaten
- Shobha Venkataraman
- Shubho Sengupta
- Brian Knott
- Mark Ibrahim
- Marcel Keller
- Christopher D. Manning
Citations in the corpus
- MP-SPDZ: A Versatile Framework for Multi-Party Computation (2020, background): “[41] have assessed eleven multi-party computation frameworks available in the first half of 2018.”
- CrypTen: Secure Multi-Party Computation Meets Machine Learning (2021, methodology): “Most other secure MPC frameworks [34] adopt an API that stays close to the underlying MPC protocols.”