MPC Protocol for G-module and its Application in Secure Compare and ReLU
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
S2FGL: Spatial Spectral Federated Graph Learning
토론
이 논문과 연결된 포럼 글이 아직 없습니다.