Logistic regression model training based on the approximate homomorphic encryption

Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering sec...
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Autor*in:	Kim, Andrey [verfasserIn] Song, Yongsoo Kim, Miran Lee, Keewoo Cheon, Jung Hee

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Homomorphic encryption Machine learning Logistic regression

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: BMC medical genomics - London : BioMed Central, 2008, 11(2018), Suppl 4 vom: 11. Okt.
Übergeordnetes Werk:	volume:11 ; year:2018 ; number:Suppl 4 ; day:11 ; month:10

Links:	Volltext

DOI / URN:	10.1186/s12920-018-0401-7

Katalog-ID:	SPR028474562

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allfields	10.1186/s12920-018-0401-7 doi (DE-627)SPR028474562 (SPR)s12920-018-0401-7-e DE-627 ger DE-627 rakwb eng Kim, Andrey verfasserin aut Logistic regression model training based on the approximate homomorphic encryption 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. Conclusions We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. Homomorphic encryption (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Song, Yongsoo aut Kim, Miran aut Lee, Keewoo aut Cheon, Jung Hee aut Enthalten in BMC medical genomics London : BioMed Central, 2008 11(2018), Suppl 4 vom: 11. Okt. (DE-627)559080824 (DE-600)2411865-5 1755-8794 nnns volume:11 year:2018 number:Suppl 4 day:11 month:10 https://dx.doi.org/10.1186/s12920-018-0401-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 Suppl 4 11 10
spelling	10.1186/s12920-018-0401-7 doi (DE-627)SPR028474562 (SPR)s12920-018-0401-7-e DE-627 ger DE-627 rakwb eng Kim, Andrey verfasserin aut Logistic regression model training based on the approximate homomorphic encryption 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. Conclusions We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. Homomorphic encryption (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Song, Yongsoo aut Kim, Miran aut Lee, Keewoo aut Cheon, Jung Hee aut Enthalten in BMC medical genomics London : BioMed Central, 2008 11(2018), Suppl 4 vom: 11. Okt. (DE-627)559080824 (DE-600)2411865-5 1755-8794 nnns volume:11 year:2018 number:Suppl 4 day:11 month:10 https://dx.doi.org/10.1186/s12920-018-0401-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 Suppl 4 11 10
allfields_unstemmed	10.1186/s12920-018-0401-7 doi (DE-627)SPR028474562 (SPR)s12920-018-0401-7-e DE-627 ger DE-627 rakwb eng Kim, Andrey verfasserin aut Logistic regression model training based on the approximate homomorphic encryption 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. Conclusions We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. Homomorphic encryption (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Song, Yongsoo aut Kim, Miran aut Lee, Keewoo aut Cheon, Jung Hee aut Enthalten in BMC medical genomics London : BioMed Central, 2008 11(2018), Suppl 4 vom: 11. Okt. (DE-627)559080824 (DE-600)2411865-5 1755-8794 nnns volume:11 year:2018 number:Suppl 4 day:11 month:10 https://dx.doi.org/10.1186/s12920-018-0401-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 Suppl 4 11 10
allfieldsGer	10.1186/s12920-018-0401-7 doi (DE-627)SPR028474562 (SPR)s12920-018-0401-7-e DE-627 ger DE-627 rakwb eng Kim, Andrey verfasserin aut Logistic regression model training based on the approximate homomorphic encryption 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. Conclusions We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. Homomorphic encryption (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Song, Yongsoo aut Kim, Miran aut Lee, Keewoo aut Cheon, Jung Hee aut Enthalten in BMC medical genomics London : BioMed Central, 2008 11(2018), Suppl 4 vom: 11. Okt. (DE-627)559080824 (DE-600)2411865-5 1755-8794 nnns volume:11 year:2018 number:Suppl 4 day:11 month:10 https://dx.doi.org/10.1186/s12920-018-0401-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 Suppl 4 11 10
allfieldsSound	10.1186/s12920-018-0401-7 doi (DE-627)SPR028474562 (SPR)s12920-018-0401-7-e DE-627 ger DE-627 rakwb eng Kim, Andrey verfasserin aut Logistic regression model training based on the approximate homomorphic encryption 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. Conclusions We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. Homomorphic encryption (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213 Song, Yongsoo aut Kim, Miran aut Lee, Keewoo aut Cheon, Jung Hee aut Enthalten in BMC medical genomics London : BioMed Central, 2008 11(2018), Suppl 4 vom: 11. Okt. (DE-627)559080824 (DE-600)2411865-5 1755-8794 nnns volume:11 year:2018 number:Suppl 4 day:11 month:10 https://dx.doi.org/10.1186/s12920-018-0401-7 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2018 Suppl 4 11 10
language	English
source	Enthalten in BMC medical genomics 11(2018), Suppl 4 vom: 11. Okt. volume:11 year:2018 number:Suppl 4 day:11 month:10
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institution	findex.gbv.de
topic_facet	Homomorphic encryption Machine learning Logistic regression
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authorswithroles_txt_mv	Kim, Andrey @@aut@@ Song, Yongsoo @@aut@@ Kim, Miran @@aut@@ Lee, Keewoo @@aut@@ Cheon, Jung Hee @@aut@@
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For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. 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author	Kim, Andrey
spellingShingle	Kim, Andrey misc Homomorphic encryption misc Machine learning misc Logistic regression Logistic regression model training based on the approximate homomorphic encryption
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topic_title	Logistic regression model training based on the approximate homomorphic encryption Homomorphic encryption (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Logistic regression (dpeaa)DE-He213
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title	Logistic regression model training based on the approximate homomorphic encryption
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title_full	Logistic regression model training based on the approximate homomorphic encryption
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author_browse	Kim, Andrey Song, Yongsoo Kim, Miran Lee, Keewoo Cheon, Jung Hee
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title_sort	logistic regression model training based on the approximate homomorphic encryption
title_auth	Logistic regression model training based on the approximate homomorphic encryption
abstract	Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. Conclusions We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. © The Author(s) 2018
abstractGer	Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. Conclusions We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. © The Author(s) 2018
abstract_unstemmed	Background Security concerns have been raised since big data became a prominent tool in data analysis. For instance, many machine learning algorithms aim to generate prediction models using training data which contain sensitive information about individuals. Cryptography community is considering secure computation as a solution for privacy protection. In particular, practical requirements have triggered research on the efficiency of cryptographic primitives. Methods This paper presents a method to train a logistic regression model without information leakage. We apply the homomorphic encryption scheme of Cheon et al. (ASIACRYPT 2017) for an efficient arithmetic over real numbers, and devise a new encoding method to reduce storage of encrypted database. In addition, we adapt Nesterov’s accelerated gradient method to reduce the number of iterations as well as the computational cost while maintaining the quality of an output classifier. Results Our method shows a state-of-the-art performance of homomorphic encryption system in a real-world application. The submission based on this work was selected as the best solution of Track 3 at iDASH privacy and security competition 2017. For example, it took about six minutes to obtain a logistic regression model given the dataset consisting of 1579 samples, each of which has 18 features with a binary outcome variable. Conclusions We present a practical solution for outsourcing analysis tools such as logistic regression analysis while preserving the data confidentiality. © The Author(s) 2018
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title_short	Logistic regression model training based on the approximate homomorphic encryption
url	https://dx.doi.org/10.1186/s12920-018-0401-7
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author2	Song, Yongsoo Kim, Miran Lee, Keewoo Cheon, Jung Hee
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up_date	2024-07-03T19:39:31.272Z
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Logistic regression model training based on the approximate homomorphic encryption

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