对人口普查规模实体解决方案研究的模拟数据没有隐私限制:通过基于个人的建模生成的大规模数据集
Beatrix Haddock1, Alix Pletcher1, Nathaniel Blair-Stahn1
1Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, 98195, USA.
Gates open research
|October 30, 2024
概括
伪人群包生成现实的,模拟的人口数据用于实体解决方案 (ER) 研究. 这使得算法开发无需使用敏感的个人信息,克服了数据科学中的数据访问障碍.
科学领域:
- 数据科学数据科学数据科学
- 计算社会科学 计算社会科学
背景情况:
- 实体解决方案 (ER) 对于数据集成至关重要,但受到围绕个人身份信息 (PII) 的隐私担忧的阻碍.
- 对访问真实PII的限制会减缓新ER方法和软件的开发和测试.
- 伪人python包通过生成用于ER研究的模拟数据集来解决这个问题.
研究的目的:
- 开发和发布一种工具,用于为实体解决方案 (ER) 生成现实的,杂的模拟人口数据.
- 让研究人员能够开发和测试ER算法,而不影响敏感的个人信息.
主要方法:
- 利用Vivarium模拟平台创建个人,家庭,家庭和就业的动态模型.
- 生成模拟人口普查,调查和行政数据,反映真实世界人口动态.
- 开发了pseudopeople Python包,用于为模拟数据添加可配置的噪声,以实现现实的ER挑战.
主要成果:
- 制作了超过900千兆字节的模拟人口数据,代表数亿个人.
- 通过伪人群包公开提供数千人的样本人口.
- 根据要求,提供访问更大的模拟群体,结构化为ER研究.
结论:
- 伪人包及其相关的模拟数据克服了ER研究中的PII访问障碍.
- 促进开发,测试和采用新的ER算法和用于大规模人口数据的软件.
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