I have a new paper out on arXiv with my colleagues at UMass Amherst (Ryan McKenna, Daniel Sheldon, and Gerome Miklau). This paper proposes a new method for generating differentially private synthetic data that’s tailored to one’s use case by only capturing information from the desired marginal distributions. This approach builds on prior work on generating private synthetic data by representing a data distribution as a probabilistic graphical model. See this post for a summary of current techniq...