A Pragmatic Comparison of Cryptographic Computation Technologies for Machine Learning

TL;DR

This paper compares cryptographic methods SMPC and FHE for secure machine learning, analyzing their theoretical foundations, software implementations, and benchmarking performance across different models and hardware.

Contribution

It provides a comprehensive comparison of SMPC and FHE, including theoretical insights, software benchmarks, and practical guidance for selecting secure computation methods in machine learning.

Findings

FHE outperforms SMPC for regression tasks.

SMPC is more efficient for complex models like CNNs.

FHE may be faster for simple dense networks with GPUs or hybrid models.

Abstract

As security demands increase, the importance of secure computation technologies grows, yet these technologies can often seem overwhelming to practitioners. Furthermore, many approaches focus only on a single technology, potentially overlooking superior alternatives. This work aims to address the issue of selecting the right technology for secure computation by presenting a comparative analysis of two highly relevant cryptographic methods and their software implementations, with a particular focus on machine learning. Firstly, we provide a theoretical summary and comparison of the secure computation paradigms of secure multi-party computation (SMPC) and fully homomorphic encryption (FHE). We outline the advantages and limitations of the protocols, as well as the relevant open-source software implementations. Secondly, we present the results of extensive benchmarking of the main software frameworks identified for machine learning operations and models. Regarding the current state of the art in FHE, we observe that it outperforms SMPC for regressions. Additionally it may be faster for simple dense networks using GPUs or Hybrid Models. Conversely, SMPC showed superior performance for complex models such as CNNs. Our results should pave the way for more technology-agnostic benchmarking of secure computation technologies for machine learning, providing guidance for practitioners looking to adopt these technologies.