{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T11:18:49Z","timestamp":1788175129390,"version":"build-2803163510"},"reference-count":59,"publisher":"Wiley","issue":"1-2","license":[{"start":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T00:00:00Z","timestamp":1769040000000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Synthetic patient data has the potential to advance research in the medical field by providing privacy\u2010preserving access to data resembling sensitive personal data. Assessing the level of privacy offered is essential to ensure privacy compliance, but it is challenging in practice. Many common methods either fail to capture central aspects of privacy or result in excessive caution based on unrealistic worst\u2010case scenarios. We present a new approach to evaluating the privacy of synthetic datasets from known probability distributions based on the maximal local privacy loss. The strategy is based on measuring individual contributions to the likelihood of generating a specific synthetic dataset, to detect possibilities of reconstructing records in the original data. To demonstrate the method, we generate synthetic time\u2010to\u2010event data based on pancreatic and colon cancer data from the Cancer Registry of Norway using sequential regressions including a flexible parametric survival model. This illustrates the method's ability to measure information leakage at an individual level, which can be used to ensure acceptable privacy risks for every patient in the data.<\/jats:p>","DOI":"10.1002\/sim.70376","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T16:21:36Z","timestamp":1769098896000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Maximal Local Privacy Loss\u2014A New Method for Privacy Evaluation of Synthetic Datasets"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6283-2393","authenticated-orcid":false,"given":"Sigrid","family":"Leithe","sequence":"first","affiliation":[{"name":"Department of Registration Cancer Registry of Norway, Norwegian Institute of Public Health  Oslo Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bj\u00f8rn","family":"M\u00f8ller","sequence":"additional","affiliation":[{"name":"Department of Registration Cancer Registry of Norway, Norwegian Institute of Public Health  Oslo Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bjarte","family":"Aagnes","sequence":"additional","affiliation":[{"name":"Department of Registration Cancer Registry of Norway, Norwegian Institute of Public Health  Oslo Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yngvar","family":"Nilssen","sequence":"additional","affiliation":[{"name":"Department of Registration Cancer Registry of Norway, Norwegian Institute of Public Health  Oslo Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul C.","family":"Lambert","sequence":"additional","affiliation":[{"name":"Department of Registration Cancer Registry of Norway, Norwegian Institute of Public Health  Oslo Norway"},{"name":"Medical Epidemiology and Biostatistics Karolinska Institutet  Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4645-1635","authenticated-orcid":false,"given":"Tor \u00c5ge","family":"Myklebust","sequence":"additional","affiliation":[{"name":"Department of Registration Cancer Registry of Norway, Norwegian Institute of Public Health  Oslo Norway"},{"name":"Department of Research and Innovation M\u00f8re and Romsdal Hospital Trust  \u00c5lesund Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12874\u2010020\u201000977\u20101"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1158\/1055\u20109965.EPI\u201017\u20100172"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3501813"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000082"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105413"},{"key":"e_1_2_9_7_1","unstructured":"J.Jordon L.Szpruch F.Houssiau et al. \u201cSynthetic Data\u00a0\u2013\u00a0What Why and How? \u201dEprint arXiv:2205.03257 (2022)."},{"key":"e_1_2_9_8_1","volume-title":"Are Synthetic Health Data Personal Data? 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