Quantum Safe Multi-Factor User Authentication Protocol for Cloud-Assisted Medical IoT

Aim:

          To design and implement a Quantum-Safe Multi-Factor User Authentication Protocol for Cloud-Assisted Medical IOT systems, ensuring secure, privacy-preserving, and tamper-resistant access to sensitive healthcare data, even against future quantum-computing attacks, by integrating post-quantum cryptography.

Abstract:

           This work presents a secure and intelligent healthcare monitoring system that leverages post-quantum cryptography to ensure long-term data protection against emerging quantum computing threats. Patient health data is continuously collected, encrypted, and stored securely in the cloud, safeguarding privacy and integrity. The system integrates real-time anomaly detection to identify abnormal health conditions, enabling timely alerts for medical intervention. When abnormalities are detected, doctors are notified and can invite patients for a check-up via secure communication channels. Upon patient confirmation, doctors perform examinations and utilize a Python-based machine learning model to diagnose arrhythmia with high accuracy. The resulting diagnosis is encrypted using post-quantum cryptography and stored in the cloud, where patients can access their results securely. By combining continuous monitoring, privacy-preserving data handling, quantum-resistant encryption, and machine learning–based diagnostics, the proposed system enhances healthcare efficiency while ensuring future-proof security and reliable medical decision-making.

Proposed System:

          In the proposed system, patient health data is continuously monitored and collected. This data is encrypted using a post-quantum cryptography algorithm to ensure future-proof security before being stored in the cloud. The system automatically detects abnormal situations in patient health conditions based on the collected data. When an abnormal situation is detected, the doctor’s side of the application loads a list of patients requiring attention. The doctor then sends an invitation for a check-up to the concerned patient via Gmail. The patient can respond by agreeing or disagreeing with the request. If the patient agrees, the doctor’s application loads the confirmed check-up list. During the check-up process, the doctor examines each patient and runs diagnostic tests to determine whether the patient is affected by arrhythmia, using a Python-based machine learning model. After completing the check-up, the arrhythmia diagnosis result is encrypted using post-quantum cryptography and stored securely in the cloud. Finally, patients can access the cloud application to check their diagnosis results, ensuring privacy, integrity, and protection from quantum computing threats. This system ensures secure data storage, encrypted communication, and reliable decision-making in medical diagnosis.

Advantages:

1. By using post-quantum cryptography, patient data and diagnosis results remain protected from both current and future quantum computing threats.
2. Automatic detection of abnormal health conditions allows doctors to quickly identify and prioritize patients who require immediate attention.
3. Encrypted storage in the cloud ensures that patients can privately access their diagnosis anytime, maintaining data integrity and confidentiality.