贝氏资料合成与揭露风险量化：以消费者支出调查为例
Bayesian Data Synthesis and Disclosure Risk Quantification: An Application to the Consumer Expenditure Surveys
摘要：
发布根据数据估算的模型生成的合成数据有助于统计机构传播具有高度实用性和隐私保护的答卷人数据。在美国劳工统计局的消费者支出调查（CE）中传播包含地理信息的敏感变量的挑战，我们提出了两个非参数贝叶斯模型作为每个数据记录的县标识符的数据合成器：贝叶斯潜在类模型和贝叶斯面积模型。这两种数据合成器都使用Dirichlet Process先验来聚类具有相似特征的观测值，并允许在观测值之间借用信息。我们开发了创新的披露风险措施，以量化CE机密数据中的固有风险，以及我们提出的合成器如何改善这些数据风险。通过分别在最小和最大披露风险情景下创建披露风险的下限和上限，我们提出的固有风险度量提供了一系列可接受的披露风险，用于评估合成数据集中的风险水平。
英文摘要：
The release of synthetic data generated from a model estimated on the data helps statistical agencies disseminate respondent-level data with high utility and privacy protection. Motivated by the challenge of disseminating sensitive variables containing geographic information in the Consumer Expenditure Surveys (CE) at the U.S. Bureau of Labor Statistics, we propose two non-parametric Bayesian models as data synthesizers for the county identifier of each data record: a Bayesian latent class model and a Bayesian areal model. Both data synthesizers use Dirichlet Process priors to cluster observations of similar characteristics and allow borrowing information across observations. We develop innovative disclosure risks measures to quantify inherent risks in the confidential CE data and how those data risks are ameliorated by our proposed synthesizers. By creating a lower bound and an upper bound of disclosure risks under a minimum and a maximum disclosure risks scenarios respectively, our proposed inherent risks measures provide a range of acceptable disclosure risks for evaluating risks level in the synthetic datasets.
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