Private Synthetic Data for Multitask Learning and Marginal Queries. (arXiv:2209.07400v1 [cs.LG])

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 …

arxiv data multitask learning synthetic data