This work contributes towards the development of an efficient and scalable open-source Secure Multi-Party Computation (SMPC) protocol on machines with moderate computational resources. We use the ABY2.0 SMPC protocol implemented on the C++ based MOTION2NX framework for secure convolutional neural network (CNN) inference application with semi-honest security. Our list of contributions are as follows. Firstly, we enhance MOTION2NX by providing a tensorized version of several primitive functions including the Hadamard product, indicator function and argmax function. Our design of secure indicator function based on a novel approach that uses secure Relu function available in the baseline MOTION2NX implementation. The secure indicator function is used, in turn, as a building block for a novel implementation of secure argmax. Secondly, we also develop a novel splitting of the computations at each CNN layer into multiple configurable chunks thereby resulting in significant reduction in RAM usage. Thirdly, we adapt an existing Helper node algorithm, working in tandem with the ABY2.0 protocol, for efficient convolution computation. This algorithm not only reduces execution time but also reduces the RAM usage required to execute CNN models, but comes at a cost of an additional compute server. Moreover, the ideas presented in this paper can also be applied to secure neural network training.
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