COLLAGENE enables privacy-aware federated and collaborative genomic data analysis

  • Li, Wentao
  • Kim, Miran
  • Zhang, Kai
  • Chen, Han
  • Jiang, Xiaoqian
  • 외 1명
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초록

Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935 .

키워드

Collaborative analysisFederated model trainingGenomic data privacyData securityACMG RECOMMENDATIONSHEALTH RESEARCHCHALLENGES
제목
COLLAGENE enables privacy-aware federated and collaborative genomic data analysis
저자
Li, WentaoKim, MiranZhang, KaiChen, HanJiang, XiaoqianHarmanci, Arif
DOI
10.1186/s13059-023-03039-z
발행일
2023-09
유형
Article
저널명
Genome Biology
24
1
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1 ~ 38

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