在医疗保健中平衡隐私和绩效:敏感数据的联合学习框架
Fatima Tanveer1, Faisal Iradat1, Waseem Iqbal2
1Department of Computer Science, Institute of Business Administration, Karachi, Pakistan.
Digital health
|September 29, 2025
概括
这项研究引入了一个保护隐私的联合学习 (PPFL) 框架,用于医疗保健数据. 该PPFL系统在中风预测方面实现了高精度,同时确保了强大的数据隐私和计算效率.
科学领域:
- 人工智能的人工智能
- 医疗保健中的机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 医疗保健数据是高度敏感的,需要强有力的隐私措施.
- 联合学习 (FL) 允许在不集中数据的情况下进行协作模式培训.
- 在医疗保健FL中平衡隐私,模型性能和效率是具有挑战性的.
研究的目的:
- 为敏感的医疗保健数据设计和评估一个保护隐私的联合学习 (PPFL) 框架.
- 为了平衡强大的隐私,模型性能和计算效率.
- 促进用户对医疗保健中的机器学习应用程序的信任.
主要方法:
- 集成的差异私人随机梯度下降 (DPSGD) 进入一个联合学习 (FL) 管道.
- 评估了关于中风预测数据集的框架.
- 测量模型的实用性 (准确性,F1),隐私 (ε),资源使用和对基线的信任特征.
主要成果:
- 在中风风险预测中达到93%的准确性,最终的隐私预算为 ε 0.69.
- 展示了最小的计算开销.
- 在隐私-实用性权衡方面表现优于现有方法,并提供实时隐私反.
结论:
- 该PPFL框架使医疗保健中的机器学习能够有效和可靠地保护隐私.
- 该框架适用于资源有限的环境.
- 未来的工作包括扩展模型架构,监管调整和用户信任评估.
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