Efficient privacy-preserving whole-genome variant queries
Mete Akgün1,2,3,4, Nico Pfeifer2,5,6, Oliver Kohlbacher2,3,7
1Medical Data Privacy and Privacy-Preserving ML on Healthcare Data, Department of Computer Science, University of Tübingen, Tübingen, Germany.
Bioinformatics (Oxford, England)
|February 12, 2022
Summary
This study introduces a privacy-preserving method for querying genomic databases using secure multi-party computation. The novel approach enables efficient and secure analysis of sensitive genetic information, protecting individual privacy.
Area of Science:
- Genomics
- Bioinformatics
- Computer Science
Background:
- Genomic data is increasingly used for disease diagnosis and treatment decisions.
- Genomic data is highly sensitive and requires robust privacy protection.
- Existing methods for querying genomic databases pose risks to individual privacy.
Purpose of the Study:
- To develop a privacy-preserving method for querying genomic databases.
- To enable secure outsourcing and storage of genomic data in cloud environments.
- To ensure data privacy, query privacy, and output privacy during genomic data analysis.
Main Methods:
- Utilized secure multi-party computation (SMC) for privacy-preserving queries.
- Employed XOR-based sharing for data, query, and output privacy.
- Implemented a system with two non-colluding proxies for data outsourcing.
Main Results:
- The proposed solution efficiently queries large-scale genomic databases (e.g., 3 million variants in under 400ms).
- Queries on over a million genomes with millions of variants are feasible in minutes.
- Achieved practical response times for querying multiple databases, suitable for real-world applications.
Conclusions:
- The developed method offers a secure and efficient way to query genomic data.
- This approach supports privacy-preserving disease-gene association studies and medical research.
- It represents a significant advancement in protecting sensitive genomic information.
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