#2159. Generating Poisson-distributed differentially private synthetic data

July 2026publication date
Proposal available till 28-05-2025
4 total number of authors per manuscript3000 $

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Journal’s subject area:
Social Sciences (miscellaneous);
Statistics and Probability;
Economics and Econometrics;
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Abstract:
The aim of this paper is to help bridge the gap between disease mapping and differential privacy literatures. Following a pair of small simulation studies, we illustrate the utility of the synthetic data produced by this approach using publicly available, county-level heart disease-related death counts. This study demonstrates the benefits of the proposed approach’s flexibility with respect to heterogeneity in population sizes and event rates while motivating further research to improve its utility.
Keywords:
Bayesian methods; confidentiality; data suppression; disclosure risk; spatial data; uncertainty

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