  We provide a differentially private algorithm for producing synthetic data
simultaneously useful for multiple tasks: marginal queries and multitask
machine learning (ML). A key innovation in our algorithm is the ability to
directly handle numerical features, in contrast to a number of related prior
approaches which require numerical features to be first converted into {high
cardinality} categorical features via {a binning strategy}. Higher binning
granularity is required for better accuracy, but this negatively impacts
scalability. Eliminating the need for binning allows us to produce synthetic
data preserving large numbers of statistical queries such as marginals on
numerical features, and class conditional linear threshold queries. Preserving
the latter means that the fraction of points of each class label above a
particular half-space is roughly the same in both the real and synthetic data.
This is the property that is needed to train a linear classifier in a multitask
setting. Our algorithm also allows us to produce high quality synthetic data
for mixed marginal queries, that combine both categorical and numerical
features. Our method consistently runs 2-5x faster than the best comparable
techniques, and provides significant accuracy improvements in both marginal
queries and linear prediction tasks for mixed-type datasets.
