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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. 

Background In clinical research, important variables may be collected from multiple data sources. Physical pooling of patient-level data from multiple sources often raises several challenges, including proper protection of patient privacy and proprietary interests. We previously developed an SAS-based package to perform distributed regression—a suite of privacy-protecting methods that perform multivariable-adjusted regression analysis using only summary-level information—with horizontally partitioned data, a setting where distinct cohorts of patients are available from different data sources. We integrated the package with PopMedNet, an open-source file transfer software, to facilitate secure file transfer between the analysis center and the data-contributing sites. The feasibility of using PopMedNet to facilitate distributed regression analysis (DRA) with vertically partitioned data, a setting where the data attributes from a cohort of patients are available from different data sources, was unknown. Objective The objective of the study was to describe the feasibility of using PopMedNet and enhancements to PopMedNet to facilitate automatable vertical DRA (vDRA) in real-world settings. Methods We gathered the statistical and informatic requirements of using PopMedNet to facilitate automatable vDRA. We enhanced PopMedNet based on these requirements to improve its technical capability to support vDRA. Results PopMedNet can enable automatable vDRA. We identified and implemented two enhancements to PopMedNet that improved its technical capability to perform automatable vDRA in real-world settings. The first was the ability to simultaneously upload and download multiple files, and the second was the ability to directly transfer summary-level information between the data-contributing sites without a third-party analysis center. Conclusions PopMedNet can be used to facilitate automatable vDRA to protect patient privacy and support clinical research in real-world settings.