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Sequre: a high-performance framework for secure multiparty computation enables biomedical data sharing.

Haris Smajlović¹, Ariya Shajii², Bonnie Berger³

¹Department of Computer Science, University of Victoria, Victoria, BC, Canada.

|January 11, 2023

Summary

This summary is machine-generated.

Sequre enables secure multiparty computation (MPC) for sensitive biomedical data analysis. This high-performance Python framework accelerates bioinformatics tasks, reducing code size and enhancing data privacy.

Keywords:

Domain-specific language Genomic privacy Secure multiparty computation

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Area of Science:

Cryptography
Bioinformatics
Computational Science

Background:

Secure multiparty computation (MPC) is crucial for analyzing sensitive biomedical data while preserving privacy.
Existing MPC frameworks can be complex and lack performance optimizations for real-world applications.

Purpose of the Study:

To introduce Sequre, a user-friendly, high-performance framework for developing MPC applications.
To enhance the speed and efficiency of MPC in bioinformatics tasks.

Main Methods:

Developed Sequre with automatic compile-time optimizations.
Integrated Python programming language for ease of use and rapid development.
Applied Sequre to various bioinformatics tasks.

Main Results:

Achieved significant performance improvements in MPC applications.
Demonstrated up to 3-4 times increased speed compared to existing pipelines.
Showcased a 7-fold reduction in codebase sizes for bioinformatics tasks.

Conclusions:

Sequre offers a powerful and accessible solution for secure computation on sensitive biomedical data.
The framework's performance gains and ease of use facilitate wider adoption of MPC in bioinformatics.
Sequre effectively balances privacy preservation with computational efficiency.