ArticleProceedings of the National Academy of Sciences of the United States of America2023
An in-depth examination of requirements for disclosure risk assessment.
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 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- The counterfactual framework in Jarmin et al. is not a measure of disclosure risk of respondents.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The key role of absolute risk in the disclosure risk assessment of public data releases.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Reply to Muralidhar et al., Kenny et al., and Hotz et al.: The benefits of engagement with external research teams.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Census officials must constructively engage with independent evaluations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- 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 · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. We argue that any proposal for quantifying disclosure risk should be based on prespecified, objective criteria. We illustrate this approach to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. More research is needed, but in the near term, the counterfactual approach appears best-suited for privacy versus utility analysis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.