Enhancing privacy and authorization control scalability in the grid through ontologies

Area of Science:

• Computer Science
• Information Security
• Health Informatics

Background:

• Data Grids facilitate data sharing across research disciplines.
• Limited adoption of Data Grids for personal data (e.g., health) due to trust issues.
• Existing solutions for secure data sharing involve complex encryption and key management layers.

Purpose of the Study:

• To present a privacy-enhancing technique for Data Grids that integrates encryption with data structure.
• To develop a framework that simplifies authorization propagation for sensitive data.
• To address the trust deficit in using Data Grids for personal and health-related data.

Main Methods:

• Developed a privacy-enhancing technique leveraging encryption tied to data organization.
• Designed a framework for natural authorization propagation.
• Implemented and evaluated the technique within a medical imaging platform.

Main Results:

• The proposed technique effectively enhances privacy in Data Grids.
• Authorization propagation is simplified and integrated with data structure.
• Successful application demonstrated on a medical imaging platform, showing feasibility and effectiveness.

Conclusions:

• The novel technique improves trust and usability of Data Grids for sensitive data.
• Integrating encryption with data structure offers a more natural approach to authorization.
• The framework is adaptable to various use cases, particularly in health informatics.