Brauneck Alissa, Schmalhorst Louisa, Weiss Stefan, Baumbach Linda, Völker Uwe, Ellinghaus David, Baumbach Jan, Buchholtz Gabriele
Hamburg University Faculty of Law, University of Hamburg, Hamburg, Germany.
Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany.
Genome Biol. 2024 Jun 13;25(1):154. doi: 10.1186/s13059-024-03296-6.
Genomic data holds huge potential for medical progress but requires strict safety measures due to its sensitive nature to comply with data protection laws. This conflict is especially pronounced in genome-wide association studies (GWAS) which rely on vast amounts of genomic data to improve medical diagnoses. To ensure both their benefits and sufficient data security, we propose a federated approach in combination with privacy-enhancing technologies utilising the findings from a systematic review on federated learning and legal regulations in general and applying these to GWAS.
基因组数据对医学进步具有巨大潜力，但因其敏感性质，为遵守数据保护法律需要采取严格的安全措施。这种冲突在全基因组关联研究（GWAS）中尤为明显，该研究依赖大量基因组数据来改善医学诊断。为确保其益处和足够的数据安全性，我们结合联邦学习和隐私增强技术，提出一种联邦方法，利用对联邦学习和一般法律法规的系统综述结果，并将其应用于GWAS。
