This paper aims to develop an efficient open-source Secure Multi-Party Computation (SMPC) repository, that addresses the issue of practical and scalable implementation of SMPC protocol on machines with moderate computational resources, while aiming to reduce the execution time. We implement the ABY2.0 protocol for SMPC, providing developers with effective tools for building applications on the ABY 2.0 protocol. This article addresses the limitations of the C++ based MOTION2NX framework for secure neural network inference, including memory constraints and operation compatibility issues. Our enhancements include optimizing the memory usage, reducing execution time using a third-party Helper node, and enhancing efficiency while still preserving data privacy. These optimizations enable MNIST dataset inference in just 32 seconds with only 0.2 GB of RAM for a 5-layer neural network. In contrast, the previous baseline implementation required 8.03 GB of RAM and 200 seconds of execution time.

翻译：本文旨在开发一个高效的开源安全多方计算（SMPC）代码库，以解决在计算资源有限设备上实用化、可扩展地实现SMPC协议的问题，同时力求减少执行时间。我们针对SMPC实现了ABY2.0协议，为开发者在ABY 2.0协议上构建应用提供了有效工具。本文解决了基于C++的MOTION2NX框架在安全神经网络推理中的局限性，包括内存约束和操作兼容性问题。我们的改进包括优化内存使用、利用第三方辅助节点减少执行时间，以及在保护数据隐私的前提下提升效率。这些优化使得在仅需0.2 GB RAM的情况下，对5层神经网络的MNIST数据集推理仅需32秒。相比之下，此前基线实现需要8.03 GB RAM和200秒执行时间。