Generating Poisson-Distributed Differentially Private Synthetic Data
Generating Poisson-Distributed Differentially Private Synthetic Data
复制标题
生成泊松分布差分隐私合成数据
DOI：
10.1111/rssa.12711
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Quick, Harrison
中科院分区：
文献类型：
--
作者：
Quick, Harrison
传播合成数据可以是一种有效的手段，使敏感数据中的信息公开，同时降低披露风险。虽然存在用于合成满足正式隐私保证的数据的机制，但这些机制通常不类似于最终用户可能用于分析数据的模型。最近，已经提出使用疾病绘图文献中的方法来生成具有高实用性但没有正式隐私保证的空间参考合成数据。本文的目的是帮助弥合疾病地图和差异隐私文献之间的差距。特别是，我们概括了一种方法，用于产生差异私人合成数据目前使用的美国人口普查局的泊松分布计数数据的情况下，以适应异质性的人口规模，并允许注入有关的基本事件率的先验信息。在一对小型模拟研究之后，我们使用公开可用的县级心脏病相关死亡计数来说明这种方法产生的合成数据的实用性。这项研究表明，所提出的方法的灵活性，在人口规模和事件发生率的异质性方面的好处，同时激励进一步的研究，以提高其效用。
The dissemination of synthetic data can be an effective means of making information from sensitive data publicly available with a reduced risk of disclosure. While mechanisms exist for synthesizing data that satisfy formal privacy guarantees, these mechanisms do not typically resemble the models an end-user might use toanalysethe data. More recently, the use of methods from the disease mapping literature has been proposed to generate spatially referenced synthetic data with high utility but without formal privacy guarantees. The objective for this paper is to help bridge the gap between the disease mapping and the differential privacy literatures. In particular, we generalize an approach for generating differentially private synthetic data currently used by the US Census Bureau to the case of Poisson-distributed count data in a way that accommodates heterogeneity in population sizes and allows for the infusion of prior information regarding the underlying event rates. Following a pair of small simulation studies, we illustrate the utility of the synthetic data produced by this approach using publicly available, county-level heart disease-related death counts. This study demonstrates the benefits of the proposed approach’s flexibility with respect to heterogeneity in population sizes and event rates while motivating further research to improve its utility.