The US Decennial Census provides valuable data for both research and policy purposes. Census data are subject to a variety of disclosure avoidance techniques prior to release in order to preserve respondent confidentiality. While many are interested in studying the impacts of disclosure avoidance methods on downstream analyses, particularly with the introduction of differential privacy in the 2020 Decennial Census, these efforts are limited by a critical lack of data: The underlying "microdata," which serve as necessary input to disclosure avoidance methods, are kept confidential. In this work, we aim to address this limitation by providing tools to generate synthetic microdata solely from published Census statistics, which can then be used as input to any number of disclosure avoidance algorithms for the sake of evaluation and carrying out comparisons. We define a principled distribution over microdata given published Census statistics and design algorithms to sample from this distribution. We formulate synthetic data generation in this context as a knapsack-style combinatorial optimization problem and develop novel algorithms for this setting. While the problem we study is provably hard, we show empirically that our methods work well in practice, and we offer theoretical arguments to explain our performance. Finally, we verify that the data we produce are "close" to the desired ground truth.

翻译：美国十年一次的人口普查为研究和政策制定提供了宝贵数据。为保护受访者机密，普查数据在发布前需经过多种披露规避技术处理。尽管许多研究关注披露规避方法对下游分析的影响（尤其是2020年十年人口普查引入差分隐私后），但此类研究因关键数据缺失而受限：作为披露规避方法必要输入的底层"微观数据"处于保密状态。本研究旨在提供仅基于已发布普查统计数据生成合成微观数据的工具，从而克服这一局限——这些合成数据可作为任何披露规避算法的输入，用于评估和比较分析。我们定义了基于已发布普查统计数据的微观数据原则性分布，并设计了从该分布中采样的算法。我们将此背景下的合成数据生成问题建模为背包式组合优化问题，并针对该场景开发了新型算法。尽管研究问题在理论上具有计算难度，但实证表明我们的方法在实践中表现良好，同时我们提供了理论论证来解释算法性能。最终，我们验证了生成数据与目标真实数据具有高度"接近性"。