Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.

Area of Science:

• Bioinformatics
• Computational Biology
• Data Privacy

Background:

• Large-scale bioinformatics research necessitates multi-institutional collaboration.
• Existing data co-localization methods are inefficient and raise privacy issues.
• Secure analysis of distributed sensitive datasets is a critical challenge.

Purpose of the Study:

• To develop a privacy-preserving federated analytics framework for secure cross-institutional bioinformatics analysis.
• To enable statistical computations on encrypted data without raw data transfer.

Main Methods:

• Implemented a federated analytics framework utilizing cryptographic methods for secure computation on encrypted data.
• Performed analyses on distributed datasets without data co-localization.

Main Results:

• Validated the framework by computing polygenic risk scores and conducting meta-analyses on real-world cohorts.
• Achieved over 99.9% accuracy compared to plaintext analyses.
• Demonstrated scalable runtime performance with increasing data size and participant sites.

Conclusions:

• The proposed framework offers a feasible and secure solution for practical bioinformatics applications involving sensitive, distributed data.
• Federated analytics can be effectively applied to large-scale genomic and health data analysis while preserving privacy.