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A privacy-preserving HLA imputation method with homomorphic encryption.

Hakin Kim1, Intak Hwang2, Yongsoo Song2

  • 1Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, Republic of Korea.

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|October 7, 2025
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Summary
This summary is machine-generated.

We developed privateHLA, a secure method for Human Leukocyte Antigen (HLA) imputation using homomorphic encryption. This approach protects client data privacy and model security on cloud servers.

Keywords:
Data encryptionGeneticsImmunology

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Human Leukocyte Antigen (HLA) imputation methods predict genotypes using single nucleotide polymorphisms (SNPs).
  • Existing local methods require large, often unavailable HLA reference panels.
  • Cloud-based imputation offers convenience but raises data privacy and model security concerns.

Purpose of the Study:

  • To develop a secure Human Leukocyte Antigen (HLA) imputation method for cloud-based outsourcing.
  • To address privacy vulnerabilities associated with uploading sensitive genotype data and protecting cloud-based models.
  • To evaluate the performance of the novel secure imputation method against existing techniques.

Main Methods:

  • Development of privateHLA, the first secure HLA imputation method utilizing homomorphic encryption.
  • Implementation of secure outsourced HLA imputation on a cloud server, protecting client data and model integrity.
  • Comparison of privateHLA's accuracy and performance against plaintext-based methods like SNP2HLA and CookHLA.

Main Results:

  • privateHLA successfully performs secure HLA imputation on an outsourced server, safeguarding client data and model privacy.
  • The method demonstrated superior performance compared to SNP2HLA.
  • Accuracy was slightly lower than the plaintext-based CookHLA method, indicating a trade-off between security and precision.

Conclusions:

  • Homomorphic encryption enables secure and private cloud-based HLA imputation, mitigating risks of data misuse and model compromise.
  • privateHLA represents a significant advancement in privacy-preserving bioinformatics tools.
  • Further research may focus on optimizing accuracy to match or exceed plaintext methods while maintaining robust security.