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IFHDS: Intelligent Framework for Securing Healthcare BigData.

Youssef M Essa1, Ezz El-Din Hemdan2, Ahmed El-Mahalawy2

  • 1Senior Big Data Engineer, Idealo, Berlin, Germany. yosufessa@gmail.com.

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|March 29, 2019
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Summary

This study introduces an Intelligent Framework for Healthcare Data Security (IFHDS) to protect sensitive patient information in the cloud. The system masks personal data and encrypts sensitive data, splitting it across distributed storage for enhanced security.

Keywords:
Big dataCloud computingHealthcareSecurity

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Area of Science:

  • Health Informatics
  • Cybersecurity
  • Cloud Computing

Background:

  • Big data analytics are crucial for healthcare, requiring significant compute and storage, often in the cloud.
  • Patient data security and privacy are major concerns hindering cloud adoption in healthcare.
  • Cloud providers' potential access to sensitive data limits trust and optimal utilization.

Purpose of the Study:

  • To propose an Intelligent Framework for Healthcare Data Security (IFHDS) for securing large-scale healthcare data in cloud environments.
  • To address the critical issues of patient data privacy and security in cloud-based healthcare systems.
  • To develop a system that minimizes performance impact while enhancing data protection.

Main Methods:

  • Implementing a column-based approach for efficient data processing.
  • Employing data masking for personal information and selective encryption for sensitive data.
  • Splitting sensitive data into multiple parts based on sensitivity levels and storing them across distributed cloud storage.

Main Results:

  • The IFHDS effectively secures sensitive patient data.
  • The proposed system demonstrates an acceptable computation time compared to existing security methods.
  • Distributed storage of data fragments prevents complete record reconstruction by cloud providers.

Conclusions:

  • The IFHDS offers a viable solution for enhancing healthcare data security in cloud environments.
  • The framework balances security requirements with the need for efficient data processing.
  • This approach mitigates risks associated with cloud storage of sensitive patient information.