# Geographic Spines in the 2020 Census Disclosure Avoidance System

arXiv: https://arxiv.org/abs/2203.16654 · HTML: https://mlsanity.com/paper/2203.16654/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2022.

## Built on by (most cited first, 30 of 44)

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- [Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy](https://mlsanity.com/paper/2507.06969/) (2025)
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- [A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem](https://mlsanity.com/paper/2409.09558/) (2024)
- [A Consensus Privacy Metrics Framework for Synthetic Data](https://mlsanity.com/paper/2503.04980/) (2025)
- [Samplable Anonymous Aggregation for Private Federated Data Analysis](https://mlsanity.com/paper/2307.15017/) (2023)
- [Dimension-free Private Mean Estimation for Anisotropic Distributions](https://mlsanity.com/paper/2411.00775/) (2024)
- [Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning](https://mlsanity.com/paper/2503.10945/) (2025)
- [The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing](https://mlsanity.com/paper/2506.16666/) (2025)
- [Algorithms with More Granular Differential Privacy Guarantees](https://mlsanity.com/paper/2209.04053/) (2022)
- [Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasets](https://mlsanity.com/paper/2305.17148/) (2023)
- [The 2020 US Decennial Census is more private than you (might) think](https://mlsanity.com/paper/2410.09296/) (2024)
- [Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis](https://mlsanity.com/paper/2412.14396/) (2024)
- [Differentially private multivariate medians](https://mlsanity.com/paper/2210.06459/) (2022)
- [Answering Private Linear Queries Adaptively using the Common Mechanism](https://mlsanity.com/paper/2212.00135/) (2022)
- [Two Views of Constrained Differential Privacy: Belief Revision and Update](https://mlsanity.com/paper/2303.00228/) (2023)
- [ResidualPlanner+: a scalable matrix mechanism for marginals and beyond](https://mlsanity.com/paper/2305.08175/) (2023)
- [Adaptive Privacy Composition for Accuracy-first Mechanisms](https://mlsanity.com/paper/2306.13824/) (2023)
- [Simulation-based Bayesian Inference from Privacy Protected Data](https://mlsanity.com/paper/2310.12781/) (2023)
- [Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance](https://mlsanity.com/paper/2407.16929/) (2024)
- [Efficient and Private Marginal Reconstruction with Local Non-Negativity](https://mlsanity.com/paper/2410.01091/) (2024)
- [Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning](https://mlsanity.com/paper/2506.12553/) (2025)
- [Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective](https://mlsanity.com/paper/2601.22434/) (2026)
- [Denoising the US Census: Succinct Block Hierarchical Regression](https://mlsanity.com/paper/2603.10099/) (2026)
- [Differentially Private Auditing Under Strategic Response](https://mlsanity.com/paper/2605.07674/) (2026)

## Abstract (arXiv)

The 2020 Census Disclosure Avoidance System (DAS) is a formally private mechanism that first adds independent noise to cross tabulations for a set of pre-specified hierarchical geographic units, which is known as the geographic spine. After post-processing these noisy measurements, DAS outputs a formally private database with fields indicating location in the standard census geographic spine, which is defined by the United States as a whole, states, counties, census tracts, block groups, and census blocks. This paper describes how the geographic spine used internally within DAS to define the initial noisy measurements impacts accuracy of the output database. Specifically, tabulations for geographic areas tend to be most accurate for geographic areas that both 1) can be derived by aggregating together geographic units above the block geographic level of the internal spine, and 2) are closer to the geographic units of the internal spine. After describing the accuracy tradeoffs relevant to the choice of internal DAS geographic spine, we provide the settings used to define the 2020 Census production DAS runs.