最大本地隐私损失-合成数据集隐私评估的新方法
Sigrid Leithe1, Bjørn Møller1, Bjarte Aagnes1
1Department of Registration, Cancer Registry of Norway, Norwegian Institute of Public Health, Oslo, Norway.
Statistics in medicine
|January 22, 2026
概括
合成患者数据可以促进医学研究,同时保持隐私. 这项研究引入了一种新的方法来评估合成数据集中的隐私损失,确保个人患者数据保持保护.
科学领域:
- 医学信息学 医学信息学
- 数据 隐私 数据 隐私 数据
- 生物统计学 生物统计学
背景情况:
- 合成患者数据提供了保护隐私的研究机会.
- 当前的隐私评估方法往往不够,要么错过了关键的隐私方面,要么过于谨慎.
- 准确的隐私评估对于合规性和伦理研究至关重要.
研究的目的:
- 引入一种新的方法来评估合成数据集的隐私.
- 测量最大的本地隐私损失,以检测潜在的记录重建.
- 确保合成数据集中所有个人的隐私风险都可接受.
主要方法:
- 根据最大的本地隐私损失开发了一个新的隐私评估策略.
- 测量对合成数据集生成概率的个人贡献.
- 将该方法应用于从真实癌症注册表数据中获得的合成时间到事件数据,使用顺序回归和灵活的参数生存模型.
主要成果:
- 拟议的方法有效地衡量了个人层面的信息泄露.
- 证明了检测重建原始数据记录的可能性的能力.
- 该方法为每个患者提供了可量化的隐私风险指标.
结论:
- 新的最大本地隐私丧失方法为合成数据隐私评估提供了更准确,更不过分谨慎的方法.
- 这种方法提高了用于医学研究的合成患者数据的可靠性.
- 确保对贡献原始数据集的个人提供强有力的隐私保护.
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