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Privacy-preserving logistic regression training

BACKGROUND: Logistic regression is a popular technique used in machine learning to construct classification models. Since the construction of such models is based on computing with large datasets, it is an appealing idea to outsource this computation to a cloud service. The privacy-sensitive nature...

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Detalles Bibliográficos
Autores principales: Bonte, Charlotte, Vercauteren, Frederik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180357/
https://www.ncbi.nlm.nih.gov/pubmed/30309364
http://dx.doi.org/10.1186/s12920-018-0398-y
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author Bonte, Charlotte
Vercauteren, Frederik
author_facet Bonte, Charlotte
Vercauteren, Frederik
author_sort Bonte, Charlotte
collection PubMed
description BACKGROUND: Logistic regression is a popular technique used in machine learning to construct classification models. Since the construction of such models is based on computing with large datasets, it is an appealing idea to outsource this computation to a cloud service. The privacy-sensitive nature of the input data requires appropriate privacy preserving measures before outsourcing it. Homomorphic encryption enables one to compute on encrypted data directly, without decryption and can be used to mitigate the privacy concerns raised by using a cloud service. METHODS: In this paper, we propose an algorithm (and its implementation) to train a logistic regression model on a homomorphically encrypted dataset. The core of our algorithm consists of a new iterative method that can be seen as a simplified form of the fixed Hessian method, but with a much lower multiplicative complexity. RESULTS: We test the new method on two interesting real life applications: the first application is in medicine and constructs a model to predict the probability for a patient to have cancer, given genomic data as input; the second application is in finance and the model predicts the probability of a credit card transaction to be fraudulent. The method produces accurate results for both applications, comparable to running standard algorithms on plaintext data. CONCLUSIONS: This article introduces a new simple iterative algorithm to train a logistic regression model that is tailored to be applied on a homomorphically encrypted dataset. This algorithm can be used as a privacy-preserving technique to build a binary classification model and can be applied in a wide range of problems that can be modelled with logistic regression. Our implementation results show that our method can handle the large datasets used in logistic regression training.
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spelling pubmed-61803572018-10-18 Privacy-preserving logistic regression training Bonte, Charlotte Vercauteren, Frederik BMC Med Genomics Research BACKGROUND: Logistic regression is a popular technique used in machine learning to construct classification models. Since the construction of such models is based on computing with large datasets, it is an appealing idea to outsource this computation to a cloud service. The privacy-sensitive nature of the input data requires appropriate privacy preserving measures before outsourcing it. Homomorphic encryption enables one to compute on encrypted data directly, without decryption and can be used to mitigate the privacy concerns raised by using a cloud service. METHODS: In this paper, we propose an algorithm (and its implementation) to train a logistic regression model on a homomorphically encrypted dataset. The core of our algorithm consists of a new iterative method that can be seen as a simplified form of the fixed Hessian method, but with a much lower multiplicative complexity. RESULTS: We test the new method on two interesting real life applications: the first application is in medicine and constructs a model to predict the probability for a patient to have cancer, given genomic data as input; the second application is in finance and the model predicts the probability of a credit card transaction to be fraudulent. The method produces accurate results for both applications, comparable to running standard algorithms on plaintext data. CONCLUSIONS: This article introduces a new simple iterative algorithm to train a logistic regression model that is tailored to be applied on a homomorphically encrypted dataset. This algorithm can be used as a privacy-preserving technique to build a binary classification model and can be applied in a wide range of problems that can be modelled with logistic regression. Our implementation results show that our method can handle the large datasets used in logistic regression training. BioMed Central 2018-10-11 /pmc/articles/PMC6180357/ /pubmed/30309364 http://dx.doi.org/10.1186/s12920-018-0398-y Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Bonte, Charlotte
Vercauteren, Frederik
Privacy-preserving logistic regression training
title Privacy-preserving logistic regression training
title_full Privacy-preserving logistic regression training
title_fullStr Privacy-preserving logistic regression training
title_full_unstemmed Privacy-preserving logistic regression training
title_short Privacy-preserving logistic regression training
title_sort privacy-preserving logistic regression training
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6180357/
https://www.ncbi.nlm.nih.gov/pubmed/30309364
http://dx.doi.org/10.1186/s12920-018-0398-y
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