Bayesian Marked Point Process Modeling for Generating Fully Synthetic Public Use Data with Point-Referenced Geography

Abstract

Many data stewards collect confidential data that include fine geography. When sharing these data with others, data stewards strive to disseminate data that are informative for a wide range of spatial and non-spatial analyses while simultaneously protecting the confidentiality of data subjects' identities and attributes. Typically, data stewards meet this challenge by coarsening the resolution of the released geography and, as needed, perturbing the confidential attributes. When done with high intensity, these redaction strategies can result in released data with poor analytic quality. We propose an alternative dissemination approach based on fully synthetic data. We generate data using marked point process models that can maintain both the statistical properties and the spatial dependence structure of the confidential data. We illustrate the approach using data consisting of mortality records from Durham, North Carolina.

References in corpus (3)

• Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information
• Multiple imputation for sharing precise geographies in public use data
• A smoothing approach for masking spatial data

Cited by in corpus (7)

• Augmenting correlation structures in spatial data using deep generative models
• Generating Partially Synthetic Geocoded Public Use Data with Decreased Disclosure Risk Using Differential Smoothing
• Bayesian Data Synthesis and Disclosure Risk Quantification: An Application to the Consumer Expenditure Surveys
• Bayesian Spatial Change of Support for Count-Valued Survey Data
• A Two-Stage Cox Process Model with Spatial and Nonspatial Covariates
• Bayesian Pseudo Posterior Synthesis for Data Privacy Protection
• Privacy for Spatial Point Process Data