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Many data stewards collect confidential data that include fine geography.When sharing these data with others, data stewards strive to disseminate datathat are informative for a wide range of spatial and non-spatial analyses whilesimultaneously protecting the confidentiality of data subjects' identities andattributes. Typically, data stewards meet this challenge by coarsening theresolution of the released geography and, as needed, perturbing theconfidential attributes. When done with high intensity, these redactionstrategies can result in released data with poor analytic quality. We proposean alternative dissemination approach based on fully synthetic data. Wegenerate data using marked point process models that can maintain both thestatistical properties and the spatial dependence structure of the confidentialdata. We illustrate the approach using data consisting of mortality recordsfrom Durham, North Carolina.

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