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基于模糊算法的高维医学数据的属性加密访问控制方法.

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科学领域:

  • 医疗信息学 医疗信息学
  • 密码学 密码学 密码学 密码学
  • 数据安全 数据安全

背景情况:

  • 目前基于区块链的数据访问控制方法因隐私问题和敏感信息暴露风险而面临高维度医疗数据的挑战.
  • 区块链技术的透明度与医疗数据的严格隐私要求相冲突.

研究的目的:

  • 提出使用模糊算法来增强患者隐私的高维医学数据属性加密访问控制方法.
  • 解决现有的基于区块链的系统在保护敏感医疗信息方面的局限性.

主要方法:

  • 使用模糊加密算法来防止未经授权的访问和解密敏感的医疗数据.
  • 使用经验模式分解的相位和频率数据来评估属性稳定性和降低噪声.
  • 实现了对不同安全级别的数据属性的加密/解密的密钥配置,并计算了访问行为的信任度.

主要成果:

  • 提出的方法实现了低于60毫秒的加密访问控制时间,即使有数百万个数据属性.
  • 证明最大加密时间减少了21ms.
  • 与其他方法相比,显示了高的反攻击成功率,与其他方法相比,攻击阻力增加了8.5%.

结论:

  • 基于模糊加密的方法有效地提高了患者的隐私和安全性,用于高维的医疗数据访问控制.
  • 该系统提供有效和强大的保护,防止未经授权的访问和网络攻击.
  • 这种方法为大数据时代的安全医疗数据管理提供了一个有希望的解决方案.