  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.
