Differential privacy and the AUC

One of the things I’m always asked when giving a talk on differential privacy is “how should we interpret \epsilon?” There a lot of ways of answering this but one way that seems to make more sense to people who actually think about risk, hypothesis testing, and prediction error is through the “area under the curve” metric, or AUC. This post came out of a discussion from a talk I gave recently at Boston University, and I’d like to thank Clem Karl for the more detailed questioning.

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