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

Improving the Utility of Poisson-Distributed, Differentially Private Synthetic Data Via Prior... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Survey Statistics and Methodology Oxford University Press

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

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References (14)

Publisher
Oxford University Press
Copyright
© The Author(s) 2022. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved. For permissions, please email: journals.permissions@oup.com
ISSN
2325-0984
eISSN
2325-0992
DOI
10.1093/jssam/smac007
Publisher site
See Article on Publisher Site

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.

Journal

Journal of Survey Statistics and Methodology – Oxford University Press

Published: Apr 4, 2022

Keywords: Bayesian methods; Cancer mortality; Confidentiality; Data suppression; Disclosure risk; Spatial data

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