综合多个观测单位的调查:在印度对纵向衰老研究的应用
Joshua Snoke1, Erik Meijer2, Drystan Phillips3
1Dr. Joshua Snoke is with the Economics, Sociology, and Statistics Department, RAND Corporation, 4570 Fifth Avenue, Suite 600, Pittsburgh, PA 15213, USA.
概括
我们开发了方法来创建来自印度长度衰老研究 (LASI) 的合成数据,这是一项健康调查. 这些合成数据保持数据模式和隐私,使得更广泛的研究访问.
科学领域:
- 数据科学数据科学数据科学
- 统计 统计 统计 统计
- 公共卫生 公共卫生
背景情况:
- 印度长度衰老研究 (LASI) 是一项有价值的健康和退休调查.
- 当前的隐私法规限制了对LASI数据的访问,限制了其用于研究和教育的使用.
- 需要方法来从复杂的调查中创建保护隐私的合成数据集.
研究的目的:
- 介绍从复杂的调查中创建合成数据的方法.
- 开发一种可以公开发布的合成版本的LASI.
- 确保合成数据保持分布模式并减轻隐私风险.
主要方法:
- 开发了新的方法来综合调查数据与多个嵌套的水平的观察 (个人,夫妇,家庭).
- 纳入连续和分类变量,以及调查权重.
- 引入了一种新的方法来平衡隐私风险和数据实用性,考虑调查采样率,通过向下加权唯一可识别记录.
主要成果:
- 合成LASI数据成功地保持了原始机密数据的分布模式.
- 提出的方法在很大程度上减轻了识别和归因披露风险.
- 新的风险-效用控制方法减少了隐私风险,同时与记录粗化相比,保留了数据效用.
结论:
- 提出的方法提供了一个坚实的基础,用于生成复杂的调查的合成版本,具有多个观察单元.
- 合成LASI数据可以公开发布,扩大研究和教育机会.
- 开发的技术为社会科学研究中的数据隐私和可访问性提供了有价值的工具.
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