Abstract
In this paper, we introduce DP-EBM*, an enhanced utility version of the Differentially Private Explainable Boosting Machine (DP-EBM). DP-EBM* offers predictions for both classification and regression tasks, providing inherent explanations for its predictions while ensuring the protection of sensitive individual information via Differential Privacy. DP-EBM* has two major improvements over DP-EBM. Firstly, we develop an error measure to assess the efficiency of using privacy budget, a crucial factor to accuracy, and optimize this measure. Secondly, we propose a feature pruning method, which eliminates less important features during the training process. Our experimental results demonstrate that DP-EBM* outperforms the state-of-the-art differentially private explainable models.
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