Abstract

Problem: Healthcare diagnosis systems face challenges in privacy preservation, real-time processing, and scalability, especially when using machine learning models like XGBoost. Solution: This paper proposes a streamlined privacy-preserving XGBoost model for healthcare diagnosis in edge computing environments. The model ensures data confidentiality through homomorphic encryption and lightweight computation for resource-constrained edge devices.