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.

翻译：随着安全需求的增加，安全计算技术的重要性日益凸显，然而这些技术往往使从业者感到难以把握。此外，许多方法仅关注单一技术，可能忽略了更优的替代方案。本文旨在通过比较分析两种高度相关的加密方法及其软件实现，解决安全计算技术的正确选择问题，特别聚焦于机器学习领域。首先，我们从理论上总结并比较了安全多方计算（SMPC）和全同态加密（FHE）这两种安全计算范式，概述了这些协议的优势与局限性，以及相关的开源软件实现。其次，我们针对机器学习运算和模型的主要软件框架，展示了广泛的基准测试结果。就全同态加密的当前技术水平而言，我们发现其在回归任务中优于安全多方计算，并且在使用GPU或混合模型时，对于简单的密集网络可能更快。相反，安全多方计算在诸如CNN等复杂模型上展现出更优性能。我们的结果将为机器学习安全计算技术提供更无偏向性的基准测试铺平道路，并为从业人员采用这些技术提供指导。