A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services

Area of Science:

• Biomedical Engineering
• Computer Science
• Cybersecurity

Background:

• Personalized healthcare systems increasingly rely on Internet of Things (IoT) and cloud computing, raising significant security and privacy concerns.
• Existing identity management frameworks struggle to provide robust authentication and continuous user verification in dynamic healthcare environments.
• Protecting sensitive patient data during cloud processing is a critical challenge for widespread adoption of digital health solutions.

Purpose of the Study:

• To propose a novel, secure identity management framework for IoT and cloud-based personalized healthcare systems.
• To enhance user authentication and ensure data privacy through multimodal biometric fusion and advanced encryption techniques.
• To establish a reliable and accurate continuous authentication mechanism for personalized healthcare applications.

Main Methods:

• Development of a multimodal biometric authentication framework fusing electrocardiogram (ECG) and photoplethysmogram (PPG) signals.
• Integration of centralized and federated identity access management with biometric-based continuous authentication.
• Implementation of Homomorphic Encryption (HE) to enable secure data processing and analysis in the cloud.
• Validation using a machine learning (ML) model on a dataset of 25 users.

Main Results:

• The fused ECG and PPG biometric framework achieved 100% accuracy in identifying and authenticating all 25 users.
• The proposed framework demonstrated superior performance compared to using individual ECG or PPG signals alone.
• Homomorphic Encryption ensured patient data remained encrypted during cloud-based processing, mitigating security risks.
• The system provided a fast, reliable, and secure authentication mechanism, addressing traditional security vulnerabilities.

Conclusions:

• The proposed fused biometric identity management framework significantly enhances the security and privacy of personalized healthcare systems.
• The combination of multimodal biometrics and Homomorphic Encryption offers a robust solution for secure data handling in cloud environments.
• This novel approach paves the way for more trustworthy and secure digital health platforms, improving patient data protection.