Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Area of Science:

• Genomics
• Bioinformatics
• Cryptography

Background:

• Genotype imputation is crucial for genomic data analysis, predicting missing genotypes.
• Sharing genetic data for imputation raises significant privacy concerns.
• Existing imputation services may not be trustworthy, hindering data sharing.

Purpose of the Study:

• To develop and evaluate secure genotype imputation methods using homomorphic encryption (HE).
• To ensure genetic data remains private during imputation analysis.
• To assess the accuracy and efficiency of HE-based imputation compared to non-secure methods.

Main Methods:

• Implemented secure genotype imputation utilizing efficient homomorphic encryption (HE) techniques.
• Compared the performance of HE-based imputation with three state-of-the-art non-secure imputation methods.
• Evaluated accuracy, time, and memory requirements of the secure and non-secure methods.

Main Results:

• HE-based genotype imputation provides robust genetic data security.
• The accuracy of HE-based methods is comparable to non-secure methods for common variants.
• HE-based methods demonstrate comparable or lower time and memory requirements.

Conclusions:

• Homomorphic encryption enables secure and practical genotype imputation for high-throughput genetic data.
• HE-based methods address privacy concerns without compromising imputation accuracy or efficiency.
• This work facilitates secure genomic data analysis, protecting individual privacy.