保护医疗保健中联合学习的隐私
Sarthak Pati1,2, Sourav Kumar3, Amokh Varma3
1Center for Federated Learning in Medicine, Indiana University, Indianapolis, IN, USA.
Patterns (New York, N.Y.)
|July 31, 2024
概括
联合学习 (FL) 可以在机构之间进行人工智能模型培训,而无需直接共享数据. 这次审查突出了FL的重点.
科学领域:
- 医疗信息学 医疗信息学
- 医学中的人工智能
- 数据隐私 数据隐私
背景情况:
- 人工智能 (AI) 有望通过数据驱动的临床见解来提高医疗保健.
- 开发强大,可泛化的人工智能模型需要广泛和多样化的数据集.
- 传统的人工智能模型开发数据共享受到法律,安全和隐私障碍的阻碍.
研究的目的:
- 审查专门针对医疗保健应用的保护隐私的联合学习 (FL) 的文献.
- 识别与医疗保健中FL相关的潜在安全和隐私风险.
- 概述这些已识别的风险的缓解策略.
主要方法:
- 对联合学习中隐私保护技术的现有研究进行系统审查.
- 在医疗保健背景下,对FL独特的安全和隐私威胁的分析.
- 在文献中讨论的缓解方法的综合.
主要成果:
- 联合学习 (FL) 允许在没有直接数据共享的情况下进行多机构AI模型培训,解决了一些隐私问题.
- 然而,FL引入了新的风险,包括从参与实体之间交换的见解和信任问题中潜在的信息泄露.
- 为了应对这些FL特有的威胁,存在各种隐私保护技术和缓解策略.
结论:
- 联合学习为医疗保健中的AI提供了一个有希望的方法,但需要仔细考虑其独特的安全和隐私挑战.
- 了解和减轻这些风险对于在临床环境中负责任地采用FL至关重要.
- 本综述是对医疗保健研究人员导航安全和隐私在佛罗里达州的指南.
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