تقرير
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census
| العنوان: | Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census |
|---|---|
| المؤلفون: | Kifer, Daniel, Abowd, John M., Ashmead, Robert, Cumings-Menon, Ryan, Leclerc, Philip, Machanavajjhala, Ashwin, Sexton, William, Zhuravlev, Pavel |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Cryptography and Security, Statistics - Methodology |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2209.03310 |
| رقم الأكسشن: | edsarx.2209.03310 |
| قاعدة البيانات: | arXiv |
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