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 \(\epsilon\)-local differential privacy, a distribution from \(Q\) whose total variation distance to \(p\) is comparable to the best such distribution.