Facilitating Federated Genomic Data Analysis by Identifying Record Correlations while Ensuring Privacy.

Area of Science:

• Genomics
• Bioinformatics
• Data Privacy

Background:

• Genomic data collection is rapidly increasing due to lower sequencing costs and widespread computing.
• Existing genomic data is fragmented and siloed across various repositories, hindering comprehensive analysis.
• Sensitive genomic data cannot be easily centralized, posing challenges for integrated research.

Purpose of the Study:

• To develop a privacy-preserving framework for identifying correlated records across distributed genomic data repositories.
• To address the challenges of data fragmentation and sensitivity in large-scale genomic data analysis.
• To enable the detection of crucial data correlations that could impact research outcomes.

Main Methods:

• The proposed framework utilizes random shuffling and synthetic record generation.
• Local differential privacy techniques are incorporated to ensure data confidentiality.
• The approach allows for a tunable trade-off between analytical accuracy and computational efficiency.

Main Results:

• The framework was evaluated using real-world genomic data from the OpenSNP dataset.
• The solution demonstrated both efficiency and effectiveness in identifying correlated records.
• The privacy-preserving approach successfully managed sensitive genomic information.

Conclusions:

• The developed framework is a significant first step towards analyzing correlated genomic data across multiple repositories securely.
• The method offers a practical solution for overcoming data silos while maintaining data privacy.
• This approach has the potential to unlock new insights in genomics research by enabling secure data integration.