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Secure Collapsing Method Based on Fully Homomorphic Encryption.

David Niyitegeka1, Reda Bellafqira1, Emmanuelle Genin2

  • 1INSERM UMR 1101 LaTIM, IMT Atlantique, Brest, France.

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Summary
This summary is machine-generated.

This study introduces a secure method for privacy-preserving genome-wide association studies (GWAS) in the cloud. It enables secure comparison of case and control genetic data, ensuring accurate results using advanced encryption techniques.

Keywords:
GWAScloud computinghomomorphic encryptionsecurity

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Area of Science:

  • Genomics
  • Computational Biology
  • Cryptography

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with diseases.
  • Cloud computing offers scalable infrastructure for large-scale genomic data analysis.
  • Protecting sensitive genetic data during cloud-based analysis is a significant challenge.

Purpose of the Study:

  • To develop a privacy-preserving approach for conducting genome-wide association studies (GWAS) in cloud environments.
  • To enable secure comparison of genetic data between a Genomic Research Unit (GRU) and a Genomic Research Center (GRC).
  • To ensure the confidentiality of genetic data while maintaining analytical accuracy.

Main Methods:

  • A novel secure version of the collapsing method based on a logistic regression model was developed.
  • Fully homomorphic encryption and secure multiparty computation were utilized to protect data.
  • The proposed scheme was implemented using the BGV cryptosystem and tested on real genetic data.

Main Results:

  • The proposed privacy-preserving GWAS method achieved identical results to analyses performed on unencrypted data.
  • Experimental results validated the accuracy and effectiveness of the secure collapsing method.
  • The use of BGV cryptosystem demonstrated the feasibility of homomorphic encryption in this context.

Conclusions:

  • The developed approach offers a secure and effective solution for privacy-preserving GWAS in cloud settings.
  • This method facilitates collaborative research by enabling secure data sharing and analysis between institutions.
  • The findings highlight the potential of advanced cryptographic techniques to advance genomic research while safeguarding privacy.