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CDC WONDER is a web-based tool for the dissemination of epidemiologic datacollected by the National Vital Statistics System. While CDC WONDER hasbuilt-in privacy protections, they do not satisfy formal privacy protectionssuch as differential privacy and thus are susceptible to targeted attacks.Given the importance of making high-quality public health data publiclyavailable while preserving the privacy of the underlying data subjects, we aimto improve the utility of a recently developed approach for generatingPoisson-distributed, differentially private synthetic data by using publiclyavailable 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 priordistributions on county-level death rates and infer reasonable ranges forPoisson-distributed, county-level death counts. In doing so, the requirementsfor satisfying differential privacy for a given privacy budget can be reducedby several orders of magnitude, thereby leading to substantial improvements inutility. To illustrate our proposed approach, we consider a dataset comprisedof over 26,000 cancer-related deaths from the Commonwealth of Pennsylvaniabelonging to over 47,000 combinations of cause-of-death and demographicvariables such as age, race, sex, and county-of-residence and demonstrate theproposed framework's ability to preserve features such as geographic,urban/rural, and racial disparities present in the true data.

Improving the Utility of Poisson-Distributed, Differentially Private  Synthetic Data via Prior Predictive Truncation with an Application to CDC  WONDER