一个全面的保护隐私的联合学习计划,为医疗事物互联网提供安全的身份验证和聚合
IEEE journal of biomedical and health informatics
|August 23, 2023
概括
医疗物联网 (IoMT) 中的联合学习面临隐私风险. 本研究介绍了一种使用盲目掩盖和无证书代理重新加密的新方案,以增强 IoMT 系统的数据安全性和隐私.
科学领域:
- 计算机科学 计算机科学
- 医疗信息学 医疗信息学
- 网络安全 网络安全
背景情况:
- 医疗物联网 (IoMT) 产生了大量的数据,需要先进的数据挖掘和集成.
- 联合学习提供了一种保护隐私的方法,用于在分布式医疗数据上培训模型.
- 现有的联合学习方法容易受到隐私泄露和安全威胁的影响,特别是在医疗保健领域.
研究的目的:
- 为IoMT系统量身定制,提出一个全面的保护隐私的联合学习方案.
- 解决当前联合学习实现中固有的安全漏洞和隐私泄露风险.
- 在资源有限的IoMT环境中提高联合学习的安全性和效率.
主要方法:
- 实施一项新的联合学习计划,包括盲目掩盖.
- 使用无证书代理重新加密 (CL-PRE) 进行安全的模型聚合.
- 整合了一种谨慎的脱落处理机制,以提高稳定性.
主要成果:
- 拟议的方案确保了联邦培训期间当地医疗数据的保密性.
- 实现了局部模型的安全聚合,防止隐私泄露.
- 为上传的模型提供了强大的身份验证,同时保持了用户身份隐私.
- 与现有方案相比,在功能特征和效率方面表现出卓越的性能.
结论:
- 开发的保护隐私的联合学习计划有效地保护IoMT中的敏感医疗数据.
- 该方案提供了增强的安全性,隐私和效率,使其高度适用于大规模的IoMT部署.
- 这种方法可以减轻与开放无线通道和IoMT恶意攻击相关的风险.
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