# Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census

arXiv: https://arxiv.org/abs/2209.03310 · HTML: https://mlsanity.com/paper/2209.03310/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2022.

## Built on by (most cited first, 8 of 8)

- [Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy](https://mlsanity.com/paper/2507.06969/) (2025)
- [Augment then Smooth: Reconciling Differential Privacy with Certified Robustness](https://mlsanity.com/paper/2306.08656/) (2023)
- [The 2020 US Decennial Census is more private than you (might) think](https://mlsanity.com/paper/2410.09296/) (2024)
- [Answering Private Linear Queries Adaptively using the Common Mechanism](https://mlsanity.com/paper/2212.00135/) (2022)
- [Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing (Extended Version)](https://mlsanity.com/paper/2603.12142/) (2026)
- [Towards more accurate and useful data anonymity vulnerability measures](https://mlsanity.com/paper/2403.06595/) (2024)
- [The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques](https://mlsanity.com/paper/2603.08207/) (2026)
- [A Sieve-Accelerated Quadrature Method for Exact Privacy Accounting in the 2020 U.S. Decennial Census](https://mlsanity.com/paper/2606.29835/) (2026)

## Abstract (arXiv)

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.