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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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14SCOPUS
15초록
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
- 제목
- COLLAGENE enables privacy-aware federated and collaborative genomic data analysis
- 저자
- Li, Wentao; Kim, Miran; Zhang, Kai; Chen, Han; Jiang, Xiaoqian; Harmanci, Arif
- 발행일
- 2023-09
- 유형
- Article
- 저널명
- Genome Biology
- 권
- 24
- 호
- 1
- 페이지
- 1 ~ 38