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arXiv · 2106.09680

Accuracy, Interpretability, and Differential Privacy via Explainable Boosting

Harsha Nori·Rich Caruana·Zhiqi Bu·Judy Hanwen Shen·Janardhan Kulkarni
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Abstract

We show that adding differential privacy to Explainable Boosting Machines (EBMs), a recent method for training interpretable ML models, yields state-of-the-art accuracy while protecting privacy. Our experiments on multiple classification and regression datasets show that DP-EBM models suffer surprisingly little accuracy loss even with strong differential privacy guarantees. In addition to high accuracy, two other benefits of applying DP to EBMs are: a) trained models provide exact global and local interpretability, which is often important in settings where differential privacy is needed; and b) the models can be edited after training without loss of privacy to correct errors which DP noise may have introduced.

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Harsha Nori, Rich Caruana, Zhiqi Bu, Judy Hanwen Shen, Janardhan Kulkarni. 2021-06-17. Accuracy, Interpretability, and Differential Privacy via Explainable Boosting. https://arxiv.org/abs/2106.09680

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