{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T18:26:35Z","timestamp":1665253595173},"reference-count":0,"publisher":"IOS Press","license":[{"start":{"date-parts":[[2021,5,24]],"date-time":"2021-05-24T00:00:00Z","timestamp":1621814400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,24]]},"abstract":"<jats:p>The process of consolidating medical records from multiple institutions into one data set makes privacy-preserving record linkage (PPRL) a necessity. Most PPRL approaches, however, are only designed to link records from two institutions, and existing multi-party approaches tend to discard non-matching records, leading to incomplete result sets. In this paper, we propose a new algorithm for federated record linkage between multiple parties by a trusted third party using record-level bloom filters to preserve patient data privacy. We conduct a study to find optimal weights for linkage-relevant data fields and are able to achieve 99.5% linkage accuracy testing on the Febrl record linkage dataset. This approach is integrated into an end-to-end pseudonymization framework for medical data sharing.<\/jats:p>","DOI":"10.3233\/shti210062","type":"book-chapter","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T12:08:40Z","timestamp":1622117320000},"source":"Crossref","is-referenced-by-count":1,"title":["A Federated Record Linkage Algorithm for Secure Medical Data Sharing"],"prefix":"10.3233","author":[{"given":"Christian M.","family":"Heidt","sequence":"first","affiliation":[{"name":"GECKO Institute, Heilbronn University of Applied Sciences, Germany"}]},{"given":"Hauke","family":"Hund","sequence":"additional","affiliation":[{"name":"GECKO Institute, Heilbronn University of Applied Sciences, Germany"}]},{"given":"Christian","family":"Fegeler","sequence":"additional","affiliation":[{"name":"GECKO Institute, Heilbronn University of Applied Sciences, Germany"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","German Medical Data Sciences: Bringing Data to Life"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI210062","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T13:09:48Z","timestamp":1635167388000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI210062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,24]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti210062","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,24]]}}}