John M. Abowd, Robert Ashmead, Ryan Cumings-Menon, Simson Garfinkel, Micah Heineck, Christine Heiss, Robert Johns, Daniel Kifer, Philip Leclerc, Ashwin Machanavajjhala, Brett Moran, William Sexton, Matthew Spence, Pavel Zhuravlev
arXiv 19 Apr 2022 · cs.CR · 75 citations (OpenAlex)
arXiv:2204.08986 · PDF · DOI · OpenAlex · Extracted main text
The Census TopDown Algorithm (TDA) is a disclosure avoidance system using differential privacy for privacy-loss accounting. The algorithm ingests the final, edited version of the 2020 Census data and the final tabulation geographic definitions. The algorithm then creates noisy versions of key queries on the data, referred to as measurements, using zero-Concentrated Differential Privacy. Another key aspect of the TDA are invariants, statistics that the Census Bureau has determined, as matter of policy, to exclude from the privacy-loss accounting. The TDA post-processes the measurements together with the invariants to produce a Microdata Detail File (MDF) that contains one record for each person and one record for each housing unit enumerated in the 2020 Census. The MDF is passed to the 2020 Census tabulation system to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File. This paper describes the mathematics and testing of the TDA for this purpose.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Canonne, Clément L, Kamath, Gautam, Steinke, Thomas, Larochelle, H.,… (2020) The Discrete Gaussian for Differential Privacy | 0.928 | 5 | 4 | 80% |
| 2 | Abowd, John M., Ashmead, Robert, Cumings-Menon, Ryan, Garfinkel, Sim… (2021) An Uncertainty Principle is a Price of Privacy-Preserving Microdata self | 0.928 | 4 | 3 | 100% |
| 3 | Canonne, Clément L., Kamath, Gautam, Steinke, Thomas (2021) The Discrete Gaussian for Differential Privacy | 0.909 | 8 | 4 | 75% |
| 4 | Dwork, Cynthia, McSherry, Frank, Nissim, Kobbi, Smith, Adam D (2006) Calibrating Noise to Sensitivity in Private Data Analysis | 0.874 | 5 | 2 | 100% |
| 5 | Abowd, John M., Schmutte, Ian M (2015) Economic Analysis and Statistical Disclosure Limitation self | 0.843 | 3 | 3 | 100% |
| 6 | Bun, Mark, Steinke, Thomas (2016) Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds | 0.737 | 4 | 2 | 75% |
| 7 | (2021) Developing the DAS: Demonstration Data and Progress Metrics | 0.737 | 3 | 2 | 100% |
| 8 | Wright, Tommy, Irimata, Kyle (2021) Empirical Study of Two Aspects of the TopDown Algorithm Output for Redistricting: Reliability & Variability | 0.737 | 3 | 2 | 100% |
| 9 | Abowd, John M., Schmutte, Ian M (2019) An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices self | 0.644 | 2 | 2 | 100% |
| 10 | Bell, William, Schafer, Joseph (2022) Simulation Studies to Investigate Variation in Census Counts and in Census Coverage Error Using 2010 SF-1 Data and 2010 CCM Resu… | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 79 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Noisy Measurements Are Important, the Design of Census Products Is Much More Important | 0.811 | 4 | 2 |
| 2 | A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census | 0.585 | 3 | 1 |
| 3 | Partial Identification with Auxiliary Moment Restrictions | 0.405 | 1 | 1 |