Quantum-Enhanced Iris Biometrics: Advancing Privacy and Security in Healthcare Systems
Dattatray Raghunath Kale, Hitesh Shinde, Radhika Rohan Shreshthi, Amolkumar N. Jadhav, Madhav J. Salunkhe, Ajit R Patil · 2025
Healthcare systems are rapidly becoming digital, which has increased the demand for safe, dependable, and privacy-preserving authentication methods. Iris biometrics have emerged as a key component for identity verification in delicate settings due to their high accuracy and stability. However, protecting privacy and data security is a difficulty for conventional biometric systems, particularly in light of new quantum risks. For advanced healthcare security, this study suggests a revolutionary quantum-enhanced, privacy-preserving iris biometrics system. Through the integration of quantum computing technologies, the framework makes use of quantum-accelerated algorithms like Grover's algorithm for efficient template matching and quantum-safe cryptography like Quantum Key Distribution (QKD). Homomorphic encryption and federated learning are two further ways to improve privacy preservation while guaranteeing adherence to HIPAA and other healthcare data protection laws. In comparison to conventional techniques, the suggested system performs better in terms of accuracy, security, and computational efficiency when tested on benchmark iris datasets. By filling important gaps in safe and privacy-focused identity management, this research creates a novel method at the nexus of quantum computing, biometrics, and healthcare.