加强联邦学习中的隐私和安全性保护电子健康记录数据免受敌对攻击
Burra Lalitha Rajeswari1, A S N Chakravarthy1
1Department of Computer Science & Engineering, Jawaharlal Nehru Technological University Kakinada, Hyderabad, India.
Informatics for health & social care
|January 8, 2026
概括
联合学习 (FL) 通过保护电子健康记录 (EHR) 免受攻击来增强医疗保健AI. 新方法平衡数据的实用性和保密性,以实现安全,稳健的模型训练.
科学领域:
- 机器学习 机器学习
- 医疗保健信息学 医疗保健信息学
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 允许在没有直接数据共享的情况下进行协作模式培训.
- 在佛罗里达州,保护敏感的电子健康记录 (EHR) 免受敌对攻击至关重要.
- 确保安全的数据聚合,匿名化和跨机构的强有力的培训是关键的挑战.
研究的目的:
- 为了提高FL的隐私和安全性,为EHRs.
- 实施匿名化,对抗模式检测,上下文意识聚合和强大的模型培训.
- 为了确保对抗攻击的保护,同时保持HIPAA合规性.
主要方法:
- 适应加权中位数过 (AWMF) 用于降低噪音,异常值和攻击.
- 基于Cuckoo的深度卷积长期记忆 (CDC-LSTM) 增强了FL的强度.
- 联邦隐私保护网格网络 (F-PPMN) 提供安全,分散的通信.
主要成果:
- 与原始属性 (100% - 300%) 相比,匿名的EHR属性 (90% - 290%) 显示数据丰富度略有减少.
- 在FL中证明了数据实用性和机密性之间的平衡.
- 在Python软件中实现.
结论:
- 该研究成功地为FL的EHRs增强了FL的隐私和安全.
- 开发的方法提供了强大的防御对抗攻击.
- 未来的工作包括整合联合加密和多方计算以进一步推进.
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