Improved Differentially Private Regression via Gradient Boosting

Shuai Tang, Sergul Aydore, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Steven Wu

Keywords: differential privacy, linear regression, gradient boosting

Abstract: We revisit the problem of differentially private squared error linear regression. We observe that existing state-of-the-art methods are sensitive to the choice of hyperparameters --- including the ``clipping threshold'' that cannot be set optimally in a data-independent way. We give a new algorithm for private linear regression based on gradient boosting. We show that our method consistently improves over the previous state of the art when the clipping threshold is taken to be fixed without knowledge of the data, rather than optimized in a non-private way --- and that even when we optimize the hyperparameters of competitor algorithms non-privately, our algorithm is no worse and often better. Additional experiments also that our algorithm is also more robust to outliers.

Submission Number: 41