Locally Private Hypothesis Selection

We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution p and a set of k probability distributions Q, we aim to output, under the constraints of ε-local differential privacy, a distribution from Q whose total variation distance to p is comparable to the best such distribution.