以数据为导向的方法来选择临床试验数据共享的隐私参数,根据差异性隐私
Henian Chen1, Jinyong Pang1, Yayi Zhao1
1Study Design and Data Analysis, College of Public Health, University of South Florida, Tampa, FL 33612, United States.
概括
不同隐私 (DP) 可以将临床试验数据匿名化,以便安全共享. 这项研究确定了最佳隐私预算 (ε) 值,表明DP在特定的ε值上接近原始数据指标,增强数据实用性和隐私.
科学领域:
- 医学研究 医学研究
- 数据隐私 数据隐私
- 统计分析 统计分析
背景情况:
- 临床试验数据共享对于研究透明度和合作至关重要.
- 差异隐私 (DP) 是一种最先进的匿名化技术,使用隐私预算 (ε) 平衡数据隐私和准确性.
- 目前DP在临床试验数据共享中未得到充分利用.
研究的目的:
- 确定适当的隐私预算 (ε) 值,用于使用差异隐私共享临床试验数据.
- 与原始临床试验数据集相比,评估DP保护数据的准确性.
主要方法:
- 分析了使用DP的两个临床试验数据集,隐私预算 (ε) 从0.01到10.
- 在原始数据和DP估计数据之间比较关键的统计指标 (比率,赔率比率,平均值).
- 研究了增加 ε 对数据准确性和隐私的影响.
主要成果:
- 据DP估计的利率与原始利率 (6.5%) 非常接近,当 ε > 1.
- 在 ε ≥ 3 时,DP 估计的赔率比率与原始比率 (0.689) 保持一致.
- 当 ε ≥ 1 时,DP 估计的平均值是近似的原始平均值 (164.64),当 ε 增加到 5 时,观察到的收率为 ε.
结论:
- 不同的隐私证明了安全和准确的临床试验数据共享的重大前景.
- 该研究为在临床研究中DP应用的隐私预算 (ε) 值的选择提供了见解.
- 需要进行进一步的研究,以建立关于选择e为最佳隐私-实用平衡的共识.
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