{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T06:05:11Z","timestamp":1788329111064,"version":"build-2803163510"},"reference-count":74,"publisher":"National Academy of Sciences","issue":"11","license":[{"start":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T00:00:00Z","timestamp":1741219200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009633","name":"HHS | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["R01 HD043392"],"award-info":[{"award-number":["R01 HD043392"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009633","name":"HHS | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["P2C HD041023"],"award-info":[{"award-number":["P2C HD041023"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2025,3,18]]},"abstract":"<jats:p>U.S. Census Bureau officials recently reaffirmed the Bureau\u2019s ongoing efforts to replace the American Community Survey (ACS) public use microdata sample with \u201cfully synthetic\u201d data to protect respondent confidentiality. With the growth of computing power and expansion of private sector data about the population, the Census Bureau has valid concerns about confidentiality threats to public data. The current plan for fully synthetic data, however, threatens a cornerstone of the nation\u2019s scientific infrastructure. Census microdata samples are among the most frequently used sources in social science research, and they are an essential tool for policy formation and planning from the local to the national level. Synthetic census microdata are not suitable for most research and policy applications. There have been no recent attempts to quantify disclosure risk in the ACS microdata, and the sole existing study failed to establish a credible threat for positive identification of ACS respondents by external intruders. I argue that we need new empirical research to pinpoint specific vulnerabilities that could allow an intruder to determine a particular individual\u2019s confidential census responses. If significant vulnerabilities are uncovered, the Census Bureau in partnership with the research community should develop targeted methods for disclosure risk reduction that minimize damage to data usability.<\/jats:p>","DOI":"10.1073\/pnas.2424655122","type":"journal-article","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T12:04:45Z","timestamp":1741262685000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":5,"title":["The shortcomings of synthetic census microdata"],"prefix":"10.1073","volume":"122","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5353-2578","authenticated-orcid":false,"given":"Steven","family":"Ruggles","sequence":"first","affiliation":[{"name":"Department of History, University of Minnesota","place":["Minneapolis"]},{"name":"Institute for Social Research and Data Innovation, University of Minnesota","place":["Minneapolis"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2025,3,6]]},"reference":[{"key":"e_1_3_4_1_2","volume-title":"U.S. Department of Commerce Office of Statistical Policy and Standards","author":"Duncan J. 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