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Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review.

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第一作者： Alissa,Brauneck

第一单位： Hamburg University Faculty of Law, University of Hamburg, Hamburg, Germany.

作者： Alissa,Brauneck ^[1] ; Louisa,Schmalhorst ^[1] ; Mohammad Mahdi,Kazemi Majdabadi ^[2] ; Mohammad,Bakhtiari ^[2] ; Uwe,Völker ^[3] ; Jan,Baumbach ^[4] ; Linda,Baumbach ^[5] ; Gabriele,Buchholtz ^[1]

作者单位： Hamburg University Faculty of Law, University of Hamburg, Hamburg, Germany. ^[1] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany. ^[2] Interfaculty Institute of Genetics and Functional Genomics, Department of Functional Genomics, University Medicine Greifswald, Greifswald, Germany. ^[3] Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany.;Computational BioMedicine lab, University of Southern Denmark, Odense, Denmark. ^[4] Department of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. ^[5]

关键词 GDPR compliance General Data Protection Regulation compliance data protection by design data protection regulation differential privacy federated learning privacy protection privacy-preserving technologies secure multiparty computation

医学主题词人类(Humans);生物医学研究(Biomedical Research);计算机安全(Computer Security);卫生保健提供(Delivery of Health Care);隐私权(Privacy)

DOI 10.2196/41588

PMID 36995759

发布时间 2025-01-30

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准确一般字义偏差语意偏离译文作用小提交