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针对多医院数据的分散,协作和保护隐私的机器学习.

Congyu Fang1, Adam Dziedzic2, Lin Zhang3

  • 1Department of Computer Science, University of Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Canada; Vector Institute, Toronto, Canada.

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|February 20, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了为多医院数据 (DeCaPH) 提供去中心化,协作和保护隐私的ML,这是一个允许安全,多机构机器学习模型培训的框架. 在没有数据集中的情况下,DeCaPH提高了模型的通用性和性能,同时保护了患者的隐私.

关键词:
(分布式) 差异化的隐私.协作式机器学习 (ML)分权化 分权化 分权化.医疗保健的ML用于医疗保健.

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科学领域:

  • 医疗信息学 医疗信息学
  • 机器学习 机器学习
  • 数据 隐私 数据 隐私 数据

背景情况:

  • 医疗保健中的机器学习 (ML) 模型需要大量,多样化的数据集,以获得准确性和通用性.
  • 机构间医疗数据的共享受到隐私法规和后勤挑战的阻碍.
  • 协作式机器学习培训至关重要,但在不损害数据隐私的情况下很难.

研究的目的:

  • 提出一个去中心化,协作和保护隐私的ML框架,用于多医院数据分析.
  • 实现多方ML模型培训,而无需直接共享或集中数据.
  • 在合作模型开发过程中保护患者的隐私.

主要方法:

  • 引入了针对多医院数据 (DeCaPH) 的去中心化,协作和保护隐私的ML.
  • 实现了无需数据传输的协作培训,确保没有数据集中.
  • 实施了隐私保护措施,以限制培训期间的信息泄露.
  • 在不依赖中央服务器的情况下,促进了培训.

主要成果:

  • 通过使用真实世界的医学数据,DeCaPH证明了对患者死亡率预测,细胞类型分类和病理识别的概括性.
  • 与非保护隐私的方法相比,使用DeCaPH训练的模型显示<3.2%的性能下降.
  • 对隐私攻击的平均脆弱性下降了高达16%.
  • DeCaPH模型的表现优于仅在私人数据上训练的模型 (提高了70%),以及以前的隐私保护方法 (提高了18.2%).

结论:

  • DeCaPH改善了公用事业隐私权的权衡,使高性能模型能够在保护数据隐私的同时实现.
  • 通过促进跨个数据集的协作,DeCaPH提高了模型的通用性.
  • 该框架有效地支持在多机构医疗保健机构中保护隐私的协作机器学习.