  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.
