{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T09:33:21Z","timestamp":1760866401663},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:00:00Z","timestamp":1648857600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:00:00Z","timestamp":1648857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Logistic regression\u00a0(LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and combine datasets from various data custodians and jurisdictions can greatly\u00a0benefit from the increased statistical power to support their analysis goals. However, combining data from different sources creates serious privacy concerns that need to be addressed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this paper, we propose two privacy-preserving protocols for performing\u00a0logistic regression with\u00a0the Newton\u2013Raphson method\u00a0in the estimation of parameters. Our proposals are based on secure Multi-Party Computation (MPC) and tailored to the\u00a0honest majority and dishonest majority security settings.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed protocols are evaluated against both synthetic and real-world datasets in\u00a0terms of\u00a0efficiency and accuracy, and a comparison is made with the ordinary logistic regression. The\u00a0experimental results demonstrate that the proposed protocols are highly efficient and accurate.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our work\u00a0introduces two\u00a0iterative algorithms to enable the distributed\u00a0training of a logistic regression model in a privacy-preserving manner. The implementation results show that our algorithms can handle large datasets from multiple sources.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12911-022-01811-y","type":"journal-article","created":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T19:02:32Z","timestamp":1648926152000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Privacy-preserving logistic regression with secret sharing"],"prefix":"10.1186","volume":"22","author":[{"given":"Ali Reza","family":"Ghavamipour","sequence":"first","affiliation":[]},{"given":"Fatih","family":"Turkmen","sequence":"additional","affiliation":[]},{"given":"Xiaoqian","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,2]]},"reference":[{"key":"1811_CR1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387","volume-title":"Applied logistic regression","author":"DW Hosmer Jr","year":"2013","unstructured":"Hosmer DW Jr, Lemeshow S, Sturdivant RX. 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