MPC Protocol for G-module and its Application in Secure Compare and ReLU
This work addresses efficiency bottlenecks in privacy-preserving deep learning by providing faster secure protocols for operations like ReLU, which is incremental but offers substantial practical gains.
The paper tackles the problem of secure comparison and selection in multi-party computation by introducing G-module as a mathematical tool to redesign protocols, resulting in communication efficiency improvements of 2X to 10X compared to state-of-the-art methods.
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.