评估美国人口普查局隐私保护方法引起的偏见和噪音
Christopher T Kenny1, Cory McCartan2, Shiro Kuriwaki3
1Department of Government, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138, USA.
Science advances
|May 1, 2024
概括
美国人口普查局.
科学领域:
- 人口统计统计数据 人口统计数据
- 避免披露统计数据的披露
- 数据隐私 数据隐私
背景情况:
- 美国人口普查局平衡数据准确性与个人隐私.
- 避免披露系统 (DAS) 对于保护敏感信息至关重要.
- 两个主要的DAS是TopDown算法 (2020年人口普查) 和交换算法 (之前的人口普查).
研究的目的:
- 独立评估人口普查局避免披露系统中的偏见和噪音.
- 为了比较TopDown和交换算法的性能.
- 评估噪声测量文件 (NMF) 对数据分析的有用性.
主要方法:
- 使用了噪音测量文件 (NMF).
- 在2010年十年人口普查数据上对TopDown算法进行了两次独立运行.
- 由TopDown和交换算法引入的评估偏差和噪声.
主要成果:
- 没有测量误差建模的NMF对于直接使用来说太杂了,特别是对于西班牙裔和多种族人口来说.
- TopDown的后处理有效地减少了NMF噪声,产生与交换算法相比的准确性.
- 两种算法的错误通常都在其他人口普查错误来源的范围内,但在人口较小的地理位置上可能很重要.
结论:
- 上下算法的后处理减轻了噪音问题,使其数据质量与交换算法相似.
- 避免披露系统引入错误,在小的地理区域更为明显.
- 在使用杂的人口普查数据时,需要仔细考虑测量误差,特别是对于特定的人口群体.
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