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Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census. (arXiv:2209.03310v1 [cs.CR])
Sept. 8, 2022, 1:20 a.m. | Daniel Kifer, John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Philip Leclerc, Ashwin Machanavajjhala, William Sexton, Pavel Zhuravlev
cs.CR updates on arXiv.org arxiv.org
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 …
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