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Generating realistic, differentially private data sets using GANs

Description

The value of individual-level data for research must be balanced against the privacy concerns of individuals. I discuss an approach for generating synthetic data sets that preserve relevant statistical properties of input data while also providing privacy guarantees for individuals in the data.

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