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Privacy-preserving genotype imputation in a trusted execution environment.

Natnatee Dokmai1, Can Kockan2, Kaiyuan Zhu2

  • 1Department of Computer Science, Indiana University, Bloomington, IN 47408, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Cell Systems
|August 27, 2021
PubMed
Summary

Genotype imputation now protects genetic privacy using secure hardware. SMac, a new algorithm, offers accurate imputation within a trusted execution environment, safeguarding data from vulnerabilities.

Keywords:
Intel SGXgenomic privacygenotype imputationimputation serverprivacy enhancing technologiesprivacy-preserving data analysissecure computationsecure enclavestrusted execution environment

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computer Science

Background:

  • Genotype imputation is crucial for genomics research, inferring missing genetic data using reference genomes.
  • Public imputation servers offer access to large-scale genomic data but raise privacy concerns due to data uploading.
  • Existing imputation methods may have vulnerabilities, necessitating enhanced security measures.

Purpose of the Study:

  • To develop a privacy-preserving genotype imputation solution using secure hardware.
  • To introduce SMac, an efficient and secure imputation algorithm for Intel SGX.
  • To demonstrate the security and accuracy of the proposed method against existing tools and vulnerabilities.

Main Methods:

  • Implementation of a secure hardware-based solution utilizing Intel SGX's trusted execution environment.
  • Development of SMac, a novel imputation algorithm optimized for Intel SGX.
  • Evaluation of imputation accuracy and security against state-of-the-art methods and known side-channel attacks.

Main Results:

  • SMac achieves imputation accuracy comparable to existing leading imputation tools.
  • The solution effectively protects input genomes within the Intel SGX trusted execution environment.
  • Vulnerabilities in existing imputation software were identified, highlighting the need for enhanced security.

Conclusions:

  • The developed hardware-based solution enables privacy-preserving genotype imputation.
  • SMac offers a scalable, accurate, and secure alternative for genomic data analysis.
  • This work advances the development of secure genomic analysis services.