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

Farnaz Farid1, Mahmoud Elkhodr2, Fariza Sabrina2

  • 1School of Computer Science, The University of Sydney, Darlington, NSW 2008, Australia.

Sensors (Basel, Switzerland)
|January 20, 2021
PubMed
Summary

This study introduces a secure identity management framework for personalized healthcare using fused electrocardiogram (ECG) and photoplethysmogram (PPG) biometrics. The novel approach ensures 100% accurate authentication, enhancing data privacy in cloud-based systems.

Keywords:
authenticationcloud computingcybersecurityfused-based biometricidentity managementinternet of thingsmachine learningpersonalized healthcareprivacysecurity

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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.