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Related Experiment Video

Updated: Dec 27, 2025

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

Sketching algorithms for genomic data analysis and querying in a secure enclave.

Can Kockan^1,2, Kaiyuan Zhu^1,2, Natnatee Dokmai¹

¹Department of Computer Science, Indiana University, Bloomington, IN, USA.

|March 6, 2020

Summary

This study introduces SkSES, a novel hardware-software approach for privacy-preserving collaborative genome-wide association studies (GWAS). SkSES significantly accelerates secure genomic data analysis, overcoming previous computational limitations.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Genome-wide association studies (GWAS) require sensitive genomic data sharing between institutions.
Existing privacy-preserving methods like secure multiparty computation (SMC) have prohibitive computational overhead for large-scale genomic data.
Sharing genomic data is crucial for rare disease research but hindered by privacy concerns.

Purpose of the Study:

To develop a computationally efficient and privacy-preserving method for collaborative GWAS.
To overcome the limitations of existing cryptographic protocols for human-genome-scale data analysis.
To enable accurate and rapid identification of significant genomic variants across institutions.

Main Methods:

Introduced SkSES, a hardware-software hybrid approach utilizing trusted execution environments (TEEs), specifically Intel SGX.
Employed novel 'sketching' algorithms to manage memory limitations within TEEs.
Incorporated efficient data compression and population stratification reduction techniques.

Main Results:

SkSES improves the running time of advanced cryptographic protocols by two orders of magnitude.
The approach enables quick, accurate, and privacy-preserving identification of top k genomic variants.
Demonstrated feasibility for human-genome-scale collaborative GWAS.

Conclusions:

SkSES offers a practical solution for privacy-preserving collaborative GWAS.
The hardware-software hybrid approach significantly reduces computational overhead.
Facilitates secure and efficient genomic data analysis for rare disease research and beyond.