SoK: General Purpose Compilers for Secure Multi-Party Computation

Abstract

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.

Secure multiparty computation

“…Thus, some compilers aim to generate the smallest circuit possible, whereas others aim to generate a circuit with the lowest depth. See Hastings et al 19 for a survey on general-purpose compilers for MPC and their usability. The combination of these advancements led to performance improvements of many orders of magnitude in just a few years, paving the way for MPC to be fast enough to be used in practice for a wide variety of problems.…”

Privacy Preserving CTL Model Checking through Oblivious Graph Algorithms

“…Recent years have seen a proliferation of work applying MPC to real-world problems, with [4] an excellent overview. This work has been enabled by developments in the efficiency of primitives [6,34,39,41,58], by the creation of usable compilers and software libraries -see [31] for a comprehensive SoK -and by increased research interest in the definition of tailored protocols. Our work fits into this narrative that MPC is practical and valuable to privacy-conscious settings [49].…”

Unique Software Engineering Techniques: Panacea for Threat Complexities in Secure Multiparty Computation (MPC) with Big Data

“…The other most important protocols are n, f, passive security, abort active security and fault tolerance active security. n represents the number of computing parties involved, f represents the maximum number of computing parties allowed to regulate and run the protocol intended in which f+1 will be a violation of the system, passive security provides a guarantee to the privacy of source data such as the number of computation parties involved, abort active security ensures that corrupt computational parties run the purported protocol and faulty tolerance active security has the role of ensuring continuous operation of the system even in instances when the computational parties have ceased to operate correctly [5]. Figure 2, shows the structure of a secure MPC and how the various parties involved compute a function using their inputs, while keeping these inputs private [14].…”