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Accuracy, Interpretability, and Differential Privacy via Explainable Boosting
Harsha Nori · Rich Caruana · Zhiqi Bu · Judy Hanwen Shen · Janardhan Kulkarni

Thu Jul 22 09:00 AM -- 11:00 AM (PDT) @

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.

Author Information

Harsha Nori (Microsoft)
Rich Caruana (Microsoft)
Zhiqi Bu (University of Pennsylvania)
Judy Hanwen Shen (Stanford)
Janardhan Kulkarni (Microsoft Research)

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