mlsanityData to 7 Oct 2026

Geographic Spines in the 2020 Census Disclosure Avoidance System

arXiv 2203.16654 ↗First posted 30 Mar 2022cs.CRcited by 42

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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.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Mar 2022.

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Citing papers per year: 2022: 7; 2023: 7; 2024: 9; 2025: 8; 2026: 11.

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  1. 2022Position: Considerations for Differentially Private Learning with Large-Scale Public Pretrainingcited by 86
  2. 2023From Robustness to Privacy and Backcited by 27
  3. 2022Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimationcited by 25
  4. 2024Nearly Tight Black-Box Auditing of Differentially Private Machine Learningcited by 19
  5. 2022Subject Membership Inference Attacks in Federated Learningcited by 18
  6. 2022Dissecting Distribution Inferencecited by 15
  7. 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debuggingcited by 12
  8. 2025Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacycited by 11
  9. 2024A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theoremcited by 8
  10. 2025A Consensus Privacy Metrics Framework for Synthetic Datacited by 8
  11. 2023Samplable Anonymous Aggregation for Private Federated Data Analysiscited by 6
  12. 2024Dimension-free Private Mean Estimation for Anisotropic Distributionscited by 6
30 more
  1. 2025Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learningcited by 6
  2. 2022Algorithms with More Granular Differential Privacy Guaranteescited by 4
  3. 2023Differentially Private Low-dimensional Synthetic Data from High-dimensional Datasetscited by 4
  4. 2024The 2020 US Decennial Census is more private than you (might) thinkcited by 4
  5. 2025The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditingcited by 4
  6. 2024Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysiscited by 3
  7. 2022Differentially private multivariate medianscited by 2
  8. 2022Answering Private Linear Queries Adaptively using the Common Mechanismcited by 2
  9. 2023Two Views of Constrained Differential Privacy: Belief Revision and Updatecited by 2
  10. 2023ResidualPlanner+: a scalable matrix mechanism for marginals and beyondcited by 2
  11. 2023Adaptive Privacy Composition for Accuracy-first Mechanismscited by 2
  12. 2023Simulation-based Bayesian Inference from Privacy Protected Datacited by 1
  13. 2024Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliancecited by 1
  14. 2024Efficient and Private Marginal Reconstruction with Local Non-Negativitycited by 1
  15. 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
  16. 2026Denoising the US Census: Succinct Block Hierarchical Regressioncited by 1
  17. 2026Differentially Private Auditing Under Strategic Responsecited by 1
  18. 2024Differentially Private Range Queries with Correlated Input Perturbationcited by 0
  19. 2025Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learningcited by 0
  20. 2025Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graphcited by 0
  21. 2025Differential privacy from axiomscited by 0
  22. 2025An Efficient Gradient-Based Inference Attack for Federated Learningcited by 0
  23. 2026Near-Optimal Private Tests for Simple and MLR Hypothesescited by 0
  24. 2026Should I use Synthetic Data for That? An Analysis of the Suitability of Synthetic Data for Data Sharing and Augmentationcited by 0
  25. 2026Sequential Auditing for f-Differential Privacycited by 0
  26. 2026Differentially Private Model Mergingcited by 0
  27. 2026Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Treescited by 0
  28. 2026Fixed-Parameter Tractability of Private Synthetic Data Generationcited by 0
  29. 2026A Sieve-Accelerated Quadrature Method for Exact Privacy Accounting in the 2020 U.S. Decennial Censuscited by 0
  30. 2026Portable Causal Fairness Across Synthetic Data Generator Familiescited by 0