Canonical Noise Distributions and Private Hypothesis Tests

Jordan Awan, Salil Vadhan

• Keywords: differential privacy, p-values, frequentist inference, optimal mechanism
• TL;DR: "Canonical noise distribution" captures whether a mechanism fully uses the privacy budget, and leads to optimal private hypothesis tests.
• Abstract: In the setting of $f$-DP, we propose the concept \emph{canonical noise distribution} (CND) which captures whether an additive privacy mechanism is tailored for a given $f$, and give a construction of a CND for an arbitrary tradeoff function $f$. We show that private hypothesis tests are intimately related to CNDs, allowing for the release of private $p$-values at no additional privacy cost as well as the construction of uniformly most powerful (UMP) tests for binary data. We apply our techniques to difference of proportions testing.
• Paper Under Submission: The paper is NOT under submission at NeurIPS