Bayesian Data Synthesis and Disclosure Risk Quantification: An Application to the Consumer Expenditure Surveys

Understand

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.