  The purpose of this paper is to guide interpretation of the semantic privacy
guarantees for some of the major variations of differential privacy, which
include pure, approximate, R\'enyi, zero-concentrated, and $f$ differential
privacy. We interpret privacy-loss accounting parameters, frequentist
semantics, and Bayesian semantics (including new results). The driving
application is the interpretation of the confidentiality protections for the
2020 Census Public Law 94-171 Redistricting Data Summary File released August
12, 2021, which, for the first time, were produced with formal privacy
guarantees.
