Ensuring Security and Privacy in Digital Healthcare System

The rapid digitization of healthcare via IoT and AI enhances patient care but introduces critical security and privacy risks. This paper investigates advanced multi-factor authentication (MFA) techniques-such as context-aware models, federated learning, behavioral biometrics, and quantum-safe cryptography-for securing Healthcare IoT (HIoT) systems. Lightweight solutions like anomaly detection and certificateless encryption are evaluated for resourceconstrained environments. To quantify system effectiveness, a novel mathematical equation is proposed, integrating authentication strength, trust, encryption resilience, and detection accuracy. This unified model enables adaptive risk assessment. The study concludes with a privacy-preserving framework that addresses real-world challenges in securing next-generation digital healthcare.