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Abstract The dissemination of synthetic data can be an effective means of making information from sensitive data publicly available with a reduced risk of disclosure. While mechanisms exist for synthesizing data that satisfy formal privacy guarantees, these mechanisms do not typically resemble the models an end-user might use to analyse the data. More recently, the use of methods from the disease mapping literature has been proposed to generate spatially referenced synthetic data with high utility but without formal privacy guarantees. The objective for this paper is to help bridge the gap between the disease mapping and the differential privacy literatures. In particular, we generalize an approach for generating differentially private synthetic data currently used by the US Census Bureau to the case of Poisson-distributed count data in a way that accommodates heterogeneity in population sizes and allows for the infusion of prior information regarding the underlying event rates. 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.
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A Latent Class Modeling Approach for Differentially Private Synthetic Data for Contingency Tables
We present an approach to construct differentially private synthetic data for contingency tables. The algorithm achieves privacy by adding noise to selected summary counts, e.g., two-way margins of the contingency table, via the Geometric mechanism. We posit an underlying latent class model for the counts, estimate the parameters of the model based on the noisy counts, and generate synthetic data using the estimated model. This approach allows the agency to create multiple imputations of synthetic data with no additional privacy loss, thereby facilitating estimation of uncertainty in downstream analyses. We illustrate the approach using a subset of the 2016 American Community Survey Public Use Microdata Sets.
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- PAR ID:
- 10353306
- Date Published:
- Journal Name:
- Journal of Privacy and Confidentiality
- Volume:
- 12
- Issue:
- 1
- ISSN:
- 2575-8527
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.29012/jpc.768
