TY  - JOUR
AU  - K Manikanta
AU  - G Priyanka
AU  - S Saifulla
AU  - J Raviteja
AU  - V Sthuthikumar
AU  - S Saif Ali
PY  - 2025
DA  - 2025/04/22
TI  - Lightweight Privacy-Preserving Medical Diagnosis in Edge Computing
JO  - Global Journal of Engineering Innovations and Interdisciplinary Research
VL  - 5
IS  - 2
AB  - 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.
SN  - 3066-1226
UR  - https://dx.doi.org/10.33425/3066-1226.1107
DO  - 10.33425/3066-1226.1107