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High dimensional learning is a perennial problem due to challenges posed by the “curse of dimensionality”; learning typically demands more computing resources as well as more training data. In differentially private (DP) settings, this is further ...

Differentially private decision tree algorithms have been popular since the introduction of differential privacy. While many private tree-based algorithms have been proposed for supervised learning tasks, such as classification, very few extend ...