Embedding Risk-Based Anonymization into Data Access Control for Providing Individual-Level Health Data in a Secure...

Area of Science:

• Biomedical Informatics
• Data Security
• Access Control

Background:

• Individual-level data in biomedical research requires robust protection due to patient sensitivity.
• Traditional access control models face limitations in balancing data re-use with privacy preservation.

Purpose of the Study:

• To present a novel architecture for secure scientific data re-use in biomedical research.
• To enhance data privacy through a combination of attribute-based access control and risk-based anonymization.

Main Methods:

• Implementation of a Usage Control (UCON) model-based architecture.
• Augmentation with risk-based anonymization using the R package sdcMicro.
• Integration with an extensible Access Control Markup Language (XACML) environment and authzforce policy decision point.

Main Results:

• The proposed architecture effectively integrates UCON policies with advanced anonymization techniques.
• The system provides a flexible and extensible framework for managing data access and privacy.

Conclusions:

• The developed architecture offers a promising solution for enabling secure and privacy-preserving data re-use in biomedical research.
• This approach facilitates the derivation of new insights from sensitive data while upholding stringent privacy standards.