New Paper - Efficient and Private Marginal Reconstruction with Local Non-Negativity

bcmullins.github.io

I have a new paper out with my colleagues from UMass Amherst and Penn State: Efficient and Private Marginal Reconstruction with Local Non-Negativity. Marginals are statistics that capture low-dimensional structure and correlations among sets of attributes in a dataset and are an important building block for differentially private algorithms. A marginal can be decomposed into a set of queries called residuals. Our paper studies how to decompose noisy answers to marginals into noisy answers to residuals and how to recombine noisy answers to many residuals into noisy answers to marginals.