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针对1:M微数据的背景知识攻击改进了天使化技术.

Rabeeha Fazal1, Razaullah Khan2, Adeel Anjum3

  • 1Department of Computer Science, COMSATS Institute of Information Technology, Islamabad, Pakistan.

PeerJ. Computer science
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概括
此摘要是机器生成的。

分享电子健康记录 (EHR) 带来隐私风险. 一个新的 (θ*,k) 实用算法增强了数据集的隐私和数据实用性,每个人 (1:M) 有多个记录.

关键词:
匿名性 匿名性 匿名性物联网 (IoT) 的物联网 (IoT) 的物联网.隐私 隐私 隐私 隐私 隐私 隐私安全的安全的安全的安全的安全.

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科学领域:

  • 医疗信息学 医疗信息学
  • 数据 隐私 数据 隐私 数据
  • 计算机科学 计算机科学

背景情况:

  • 跨组织共享电子健康记录 (EHR) 为医疗治疗和研究提供了好处,但也引发了重大隐私问题.
  • 传统的隐私模型通常假定每个人只有一条记录 (1:1数据集),这对于每个人多条记录 (1:M数据集) 的现实场景是不够的.
  • 现有的隐私模型,如 θ-Sensitive k-Anonymity, (p,l) -angelization,和 (k,l) -diversity,表明1:M数据集的高功用损失和不充分的隐私.

研究的目的:

  • 解决当前隐私模型在处理1M数据集方面的局限性.
  • 提出一种新的算法,平衡1:M数据集的增强隐私与数据实用性.
  • 评估拟议的算法的有效性与现有方法相比.

主要方法:

  • 该研究分析了现有的隐私模型 (θ-敏感的k-匿名性, (p, l) 角化, (k, l) 多样性) 对1:M数据集的不适用性和高效益损失.
  • 提出了一个新的算法, (θ*,k) -utility,以改善匿名1:M数据集的隐私和实用性保护.
  • 使用现实世界数据集进行实验,以将拟议的方法与现有方法进行比较.

主要成果:

  • 与现有模型相比,提出的 (θ*,k) 实用性算法在保护 1:M 数据集的隐私和数据实用性方面表现出卓越的性能.
  • 当前的模型显示,当应用到1:M数据集时,显著的实用性损失和隐私漏洞.
  • 实验结果验证了新算法在现实数据上的有效性.

结论:

  • (θ*,k) 实用算法为1M EHR数据集的隐私保护数据共享提供了一个强大的解决方案.
  • 这些发现凸显了传统隐私模型对于复杂,多记录数据集的不足.
  • 这项研究有助于开发医疗保健中更安全,更实用的数据共享实践.