  CDC WONDER is a web-based tool for the dissemination of epidemiologic data
collected by the National Vital Statistics System. While CDC WONDER has
built-in privacy protections, they do not satisfy formal privacy protections
such as differential privacy and thus are susceptible to targeted attacks.
Given the importance of making high-quality public health data publicly
available while preserving the privacy of the underlying data subjects, we aim
to improve the utility of a recently developed approach for generating
Poisson-distributed, differentially private synthetic data by using publicly
available information to truncate the range of the synthetic data.
Specifically, we utilize county-level population information from the U.S.
Census Bureau and national death reports produced by the CDC to inform prior
distributions on county-level death rates and infer reasonable ranges for
Poisson-distributed, county-level death counts. In doing so, the requirements
for satisfying differential privacy for a given privacy budget can be reduced
by several orders of magnitude, thereby leading to substantial improvements in
utility. To illustrate our proposed approach, we consider a dataset comprised
of over 26,000 cancer-related deaths from the Commonwealth of Pennsylvania
belonging to over 47,000 combinations of cause-of-death and demographic
variables such as age, race, sex, and county-of-residence and demonstrate the
proposed framework's ability to preserve features such as geographic,
urban/rural, and racial disparities present in the true data.
