Private Tabular Survey Data Products through Synthetic Microdata Generation

We propose three synthetic microdata approaches to generate private tabular survey data products for public release. We adapt a disclosure risk based-weighted pseudo posterior mechanism to survey data with a focus on producing tabular products under a formal privacy guarantee. Two of our approaches synthesize the observed sample distribution of the outcome and survey weights, jointly, such that both quantities together possess a probabilistic differential privacy guarantee. The privacy-protected outcome and sampling weights are used to construct tabular cell estimates and associated standard errors to correct for survey sampling bias. The third approach synthesizes the population distribution from the observed sample under a pseudo posterior construction that treats survey sampling weights as fixed to correct the sample likelihood to approximate that for the population. Each by-record sampling weight in the pseudo posterior is, in turn, multiplied by the associated privacy, risk-based weight for that record to create a composite pseudo posterior mechanism that both corrects for survey bias and provides a privacy guarantee for the observed sample. Through a simulation study and a real data application to the Survey of Doctorate Recipients public use file, we demonstrate that our three microdata synthesis approaches to construct tabular products provide superior utility preservation as compared to the additive-noise approach of the Laplace Mechanism. Moreover, all our approaches allow the release of microdata to the public, enabling additional analyses at no extra privacy cost.