The Role of Chance in the Census Bureau Database Reconstruction Experiment
Bibliographic Data
| ID | 11291038 |
|---|---|
| Authors | Steven Ruggle (0000-0001-5353-2578, University of Minnesota, corresponding author), David Van Riper (0000-0002-2110-2925, University of Minnesota) |
| Year | 2022 |
| Volume | 41 |
| Issue | 3 |
| Pages | 781-788 |
| Publication date | 2022-06-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Population Research and Policy Review (JOURNAL) |
| Journal identifiers | ISSN: 0167-5923 • E-ISSN: 1573-7829 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11113-021-09674-3 |
| PMID | 35692262 |
| OpenAlex | W3195610962 |
| Language | EN |
| Citations received | 14 |
| References cited | 6 |
The Census Bureau plans a new approach to disclosure control for the 2020 census that will add noise to every statistic the agency produces for places below the state level. The Bureau argues the new approach is needed because the confidentiality of census responses is threatened by “database reconstruction,” a technique for inferring individual-level responses from tabular data. The Census Bureau constructed hypothetical individual-level census responses from public 2010 tabular data and matched them to internal census records and to outside sources. The Census Bureau did not compare these results to a null model to demonstrate that their success in matching would not be expected by chance. This is analogous to conducting a clinical trial without a control group. We implement a simple simulation to assess how many matches would be expected by chance. We demonstrate that most matches reported by the Census Bureau experiment would be expected randomly. To extend the metaphor of the clinical trial, the treatment and the placebo produced similar outcomes. The database reconstruction experiment therefore fails to demonstrate a credible threat to confidentiality
Census · Database · Demographic economics · Economic growth · Economics · Geography · Population · Population statistics · Regional science · Sociology · Census and Population Estimation · Computer Science · Demography · Housing Market and Economics · Privacy-Preserving Technologies in Data
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| Unique citing works | 14 |
|---|---|
| Citations per year | 3,5 |
| Citation span | 2022 - 2026 (5) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 14 |