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.

Canonical Noise Distributions and Private Hypothesis Tests

"Too Long; Didn't Read": a short sentence describing your paper

Abstract of paper. Add TeX formulas using the following formats: $In-line Formula$ or $$Block Formula$$

Search author profile by first, middle and last name or email address. If the profile is not found, you can add the author by completing first, middle, and last names as well as author email address.

Please indicate the status of your submission

Title of paper. Add TeX formulas using the following formats: $In-line Formula$ or $$Block Formula$$

Canonical Noise Distributions and Private Hypothesis Tests

Jordan Awan, Salil Vadhan