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Journal article
Improving the Utility of Poisson-Distributed, Differentially Private Synthetic Data via Prior Predictive Truncation with an Application to CDC WONDER
Journal of survey statistics and methodology, v 10(3), pp 596-617
04 Apr 2022
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Abstract
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 US 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.
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Details
- Title
- Improving the Utility of Poisson-Distributed, Differentially Private Synthetic Data via Prior Predictive Truncation with an Application to CDC WONDER
- Creators
- Harrison Quick - Drexel University
- Publication Details
- Journal of survey statistics and methodology, v 10(3), pp 596-617
- Publisher
- Oxford University Press
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000784715800001
- Scopus ID
- 2-s2.0-85133844264
- Other Identifier
- 991019168995904721
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Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Social Sciences, Mathematical Methods
- Statistics & Probability