保护医疗保健数据:在集群环境中使用混合加密的联合学习框架
C Srivenkateswaran1, A Jaya Mabel Rani2, R Senthil Kumaran3
1Department of Artificial Intelligence and Data Science, Rajalakshmi Institute of Technology, Chennai, India.
概括
本研究介绍了一种使用圆曲线加密 (ECC) 和蛇形算法来保护集群环境中的医疗保健数据的混合加密方法. 该ECC-Serpent方案提供了增强的安全性,性能和HIPAA合规性,达到97.5%的准确性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 医疗信息学 医疗信息学
背景情况:
- 医疗保健数据是高度敏感的,需要强有力的保护.
- 现有的加密方法可能面临着可扩展性,互操作性和监管合规性 (HIPAA) 的挑战.
- 集群环境对分布式医疗保健数据提出了独特的安全挑战.
研究的目的:
- 开发和评估一个混合加密方案,结合圆曲线加密 (ECC) 和蛇算法.
- 评估该计划是否适合在集群环境中保护医疗保健数据.
- 确保拟议解决方案的可扩展性,互操作性和HIPAA合规性.
主要方法:
- 开发了一个混合加密方案,集成ECC用于密钥管理和蛇用于对称加密.
- 实施了与ECC的等级密钥管理策略,以确保安全的密钥交换.
- 使用与HIPAA法规一致的定期钥匙旋转和存储实践.
- 构建了一个Python框架来实现和测试加密方案.
主要成果:
- 通过ECC-Serpent混合加密方案,医疗数据的安全性和性能得到了提高.
- 该解决方案在集群环境中被证明是可扩展和可互操作的.
- 实施的框架在保护患者数据方面实现了97.5%的准确率.
- 该计划确保符合HIPAA法规.
结论:
- 混合ECC-Serpent加密是一种可行且有效的解决方案,用于在集群环境中保护医疗保健数据.
- 这种方法平衡了高效的密钥分配与高加密强度.
- 这种方法提高了数据完整性,患者隐私和医疗保健中的监管遵守.
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