A Novel Key Distribution for Mobile Patient Authentication Inspired by the Federated Learning Concept and Based on...

Area of Science:

• e-Healthcare Security
• Mobile Computing
• Cryptography

Background:

• Mobile patients in e-healthcare require secure communication and seamless authentication across facilities.
• Existing key distribution mechanisms face challenges with scalability and storage overhead for mobile users.
• Federated learning (FL) offers a decentralized approach that can be adapted for secure parameter sharing.

Purpose of the Study:

• To introduce a novel hierarchical key distribution architecture for mobile patient authentication in e-healthcare.
• To leverage federated learning principles for efficient and secure key management.
• To reduce storage and computational burdens on mobile patients.

Main Methods:

• A hierarchical key distribution architecture inspired by federated learning (FL).
• A central authority shares global security parameters with subordinate units.
• Local parameters are combined to generate and distribute symmetric keys to mobile patients.
• Formal security verification using SPAN-AVISPA and BAN logic for protocol analysis.

Main Results:

• The proposed FL-inspired system enables patients to store a single key, minimizing storage overhead.
• Formal verification confirmed the system's security and authentication capabilities.
• Performance analysis showed reduced computational load and fewer exchanged messages compared to traditional methods.
• Enhanced scalability and efficiency in dynamic healthcare environments.

Conclusions:

• The novel architecture provides a lightweight, scalable, and secure key distribution framework for mobile e-healthcare.
• This approach effectively addresses the critical need for robust patient authentication in cross-facility scenarios.
• The FL-inspired method significantly improves efficiency and reduces resource demands for mobile patients.