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Privacy-preserving logistic regression with secret sharing

BACKGROUND: Logistic regression (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 benefit from the increased statistical power to...

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Autores principales: Ghavamipour, Ali Reza, Turkmen, Fatih, Jiang, Xiaoqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977014/
https://www.ncbi.nlm.nih.gov/pubmed/35366870
http://dx.doi.org/10.1186/s12911-022-01811-y
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author Ghavamipour, Ali Reza
Turkmen, Fatih
Jiang, Xiaoqian
author_facet Ghavamipour, Ali Reza
Turkmen, Fatih
Jiang, Xiaoqian
author_sort Ghavamipour, Ali Reza
collection PubMed
description BACKGROUND: Logistic regression (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 benefit 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. METHODS: In this paper, we propose two privacy-preserving protocols for performing logistic regression with the Newton–Raphson method in the estimation of parameters. Our proposals are based on secure Multi-Party Computation (MPC) and tailored to the honest majority and dishonest majority security settings. RESULTS: The proposed protocols are evaluated against both synthetic and real-world datasets in terms of efficiency and accuracy, and a comparison is made with the ordinary logistic regression. The experimental results demonstrate that the proposed protocols are highly efficient and accurate. CONCLUSIONS: Our work introduces two iterative algorithms to enable the distributed training of a logistic regression model in a privacy-preserving manner. The implementation results show that our algorithms can handle large datasets from multiple sources.
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spelling pubmed-89770142022-04-04 Privacy-preserving logistic regression with secret sharing Ghavamipour, Ali Reza Turkmen, Fatih Jiang, Xiaoqian BMC Med Inform Decis Mak Research BACKGROUND: Logistic regression (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 benefit 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. METHODS: In this paper, we propose two privacy-preserving protocols for performing logistic regression with the Newton–Raphson method in the estimation of parameters. Our proposals are based on secure Multi-Party Computation (MPC) and tailored to the honest majority and dishonest majority security settings. RESULTS: The proposed protocols are evaluated against both synthetic and real-world datasets in terms of efficiency and accuracy, and a comparison is made with the ordinary logistic regression. The experimental results demonstrate that the proposed protocols are highly efficient and accurate. CONCLUSIONS: Our work introduces two iterative algorithms to enable the distributed training of a logistic regression model in a privacy-preserving manner. The implementation results show that our algorithms can handle large datasets from multiple sources. BioMed Central 2022-04-02 /pmc/articles/PMC8977014/ /pubmed/35366870 http://dx.doi.org/10.1186/s12911-022-01811-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Ghavamipour, Ali Reza
Turkmen, Fatih
Jiang, Xiaoqian
Privacy-preserving logistic regression with secret sharing
title Privacy-preserving logistic regression with secret sharing
title_full Privacy-preserving logistic regression with secret sharing
title_fullStr Privacy-preserving logistic regression with secret sharing
title_full_unstemmed Privacy-preserving logistic regression with secret sharing
title_short Privacy-preserving logistic regression with secret sharing
title_sort privacy-preserving logistic regression with secret sharing
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977014/
https://www.ncbi.nlm.nih.gov/pubmed/35366870
http://dx.doi.org/10.1186/s12911-022-01811-y
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