Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
Cryptography and Security
2022-09-09 v1 Methodology
Abstract
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 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.
Keywords
Cite
@article{arxiv.2209.03310,
title = {Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census},
author = {Daniel Kifer and John M. Abowd and Robert Ashmead and Ryan Cumings-Menon and Philip Leclerc and Ashwin Machanavajjhala and William Sexton and Pavel Zhuravlev},
journal= {arXiv preprint arXiv:2209.03310},
year = {2022}
}