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Lightweight Privacy-Preserving Medical Diagnosis in Edge Computing

Year: 2025

Type: article

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.

Source: Global Journal of Engineering Innovations and Interdisciplinary Research

DOI: 10.33425/3066-1226.1107

Authors: Manikanta K, Priyanka G, Saifulla S, Raviteja J, Sthuthikumar V, Saif Ali S

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4409904835

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