Evidence map›Paper›PMID 36800385›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Confidence-ranked reconstruction of census microdata from published statistics.

Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Reproducibility of social science research using aggregate statistics with noise infused for differential privacy.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. The 2020 US Decennial Census is more private than you (might) think.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. [Re-identification potential of structured health data].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2024
    Review
  6. When Privacy Protection Goes Wrong: How and Why the 2020 Census Confidentiality Program Failed.The journal of economic perspectives : a journal of the American Economic Association · 2024
    Article
  7. Article
  8. Confidence-ranked reconstruction of census records from aggregate statistics fails to capture privacy risks and reidentifiability.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  9. Database reconstruction does compromise confidentiality.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Travis DickDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104.
Cynthia DworkSchool of Engineering and Applied Sciences, Harvard University, Boston, MA 02134.
Michael KearnsDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104.
Terrance LiuSchool of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.
Aaron RothDepartment of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104.
Giuseppe VietriDepartment of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455.
Zhiwei Steven WuSchool of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A reconstruction attack on a private dataset

Indexed as

data privacydifferential privacyreconstruction attackU.S. Census

Identifiers

PMID36800385
PMCPMC9974405

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.