MPC Protocol for G-module and its Application in Secure Compare and ReLU

TL;DR

This paper introduces G-module based protocols to enhance secure comparison and ReLU computations in privacy-preserving deep learning, achieving significant communication efficiency improvements without relying on costly cryptographic operations.

Contribution

It pioneers the use of G-module operations in secure MPC protocols, enabling more efficient secure comparison and ReLU functionalities.

Findings

Communication cost reduced by 2X to 10X compared to existing methods.

Protocols do not require public key or other expensive cryptographic operations.

Achieved high computational efficiency in secure comparison and ReLU applications.

Abstract

Secure comparison and secure selection are two fundamental MPC (secure Multi-Party Computation) protocols. One important application of these protocols is the secure ReLU and DReLU computation in privacy preserving deep learning. In this paper, we introduce G-module, a mathematics tool, to re-design such protocols. In mathematics, given a group G, a G-module is an abelian group M on which G acts compatibly with the abelian group structure on M. We design three secure protocols for three G-module operations. i.e. "G-module action", "Cross G-module action" and "G-module recover". As far as we know, this is the first work on secure G-module operations. Based on them, we design secure comparison, selection, ReLU and DReLU protocols, which improve communication efficiency by 2X to 10X compared with state of arts. Our protocols are very computation efficient too. They do not require public key operations or any other expensive operations.