论文标题
一种潜在的类建模方法，用于生成合成数据并从差异私有计数中进行后验推断
A Latent Class Modeling Approach for Generating Synthetic Data and Making Posterior Inferences from Differentially Private Counts
论文作者
论文摘要
存在几种算法，用于创建从应急表（例如双向或三向边际计数）中创建差异私人计数。所得噪声计数通常不对应于连贯的应变表，因此，如果希望发布的计数对应于连贯的应急表，则需要一些后处理步骤。我们提出了一种潜在的类建模方法，用于后处理差异私人边缘计数，可以使用（i）从一组边际计数中创建差异性私有合成数据，（ii）以启用有关机密计数的后验推断。我们使用2016年美国社区调查的一部分公共使用微型AT和2004年国家长期护理调查来说明方法。
Several algorithms exist for creating differentially private counts from contingency tables, such as two-way or three-way marginal counts. The resulting noisy counts generally do not correspond to a coherent contingency table, so that some post-processing step is needed if one wants the released counts to correspond to a coherent contingency table. We present a latent class modeling approach for post-processing differentially private marginal counts that can be used (i) to create differentially private synthetic data from the set of marginal counts, and (ii) to enable posterior inferences about the confidential counts. We illustrate the approach using a subset of the 2016 American Community Survey Public Use Microdata Sets and the 2004 National Long Term Care Survey.
