A Queue-Poll Extension and DataSHIELD: Standardised, Monitored, Indirect and Secure Access to Sensitive Data.

Area of Science:

• Health Informatics
• Data Security
• Distributed Computing

Background:

• Privacy-preserving data analysis is crucial for multi-institutional research.
• Existing methods often require data centralization, posing security risks.
• Securely accessing distributed data without compromising privacy is a significant challenge.

Purpose of the Study:

• To implement a novel queue-poll extension for secure, indirect data access.
• To integrate this extension with DataSHIELD for standardized multi-institutional analysis.
• To facilitate the formation of trusted data analysis networks.

Main Methods:

• Developed a queue-poll extension using the HTTP protocol.
• Integrated the extension with the DataSHIELD platform.
• Implemented a system where requests are pulled into institutions for execution, ensuring data remains local.

Main Results:

• The implemented system allows for standardized, monitored, and secure indirect data access.
• New institutions can be easily integrated into the trusted network.
• Request processing is slower compared to direct push methods.

Conclusions:

• The developed service reduces barriers for institutions forming analysis networks.
• It enables privacy-preserving data analysis and sharing of results and models.
• Future work should focus on optimizing monitoring and request speed.