Multi-cloud privacy-preserving logistic regression

Clouds can significantly reduce the cost and time of business solutions. However, cloud services introduce significant security and privacy challenges when they process sensitive information. For instance, a dataset for machine learning could contain delicate information that traditional encryption...

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Bibliografski detalji
Glavni autori:	Babenko, M. G., Бабенко, М. Г.
Format:	Статья
Jezik:	English
Izdano:	Springer Science and Business Media Deutschland GmbH 2022
Teme:	Cloud security Secure multi-party computation Homomorphic encryption Privacy-preserving logistic regression Residue number system (RNS) Quality control
Online pristup:	https://dspace.ncfu.ru/handle/20.500.12258/18617
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id	ir-20.500.12258-18617
record_format	dspace
spelling	ir-20.500.12258-186172023-09-14T09:45:55Z Multi-cloud privacy-preserving logistic regression Babenko, M. G. Бабенко, М. Г. Cloud security Secure multi-party computation Homomorphic encryption Privacy-preserving logistic regression Residue number system (RNS) Quality control Clouds can significantly reduce the cost and time of business solutions. However, cloud services introduce significant security and privacy challenges when they process sensitive information. For instance, a dataset for machine learning could contain delicate information that traditional encryption approaches cannot protect during data analysis. Homomorphic Encryption (HE) schemes and secure Multi-Party Computation (MPC) are considered solutions for privacy protection in third-party infrastructures. In this paper, we propose a Multi-Cloud Logistic Regression based on Residue Number System (MC-LR-RNS) that provides security, parallel processing, and scalability. To validate the efficiency and practicability of the solution, we provide its analysis with different configurations, datasets, and cloud service providers. We use six available datasets from medicine (diabetes, cancer, drugs, etc.) and genomics. The analysis shows that MC-LR-RNS provides the same levels of quality as non-HE solutions and improved performance due to multi-cloud parallel computations. 2022-02-01T09:06:35Z 2022-02-01T09:06:35Z 2021 Статья Cortés-Mendoza, J. M., Tchernykh, A., Babenko, M. G., Pulido-Gaytán, B., Radchenko, G., Multi-cloud privacy-preserving logistic regression // Communications in Computer and Information Science. - 2021. - Том 1510 CCIS. - Стр.: 457 - 471. - DOI10.1007/978-3-030-92864-3_35 http://hdl.handle.net/20.500.12258/18617 en Communications in Computer and Information Science application/pdf Springer Science and Business Media Deutschland GmbH
institution	СКФУ
collection	Репозиторий
language	English
topic	Cloud security Secure multi-party computation Homomorphic encryption Privacy-preserving logistic regression Residue number system (RNS) Quality control
spellingShingle	Cloud security Secure multi-party computation Homomorphic encryption Privacy-preserving logistic regression Residue number system (RNS) Quality control Babenko, M. G. Бабенко, М. Г. Multi-cloud privacy-preserving logistic regression
description	Clouds can significantly reduce the cost and time of business solutions. However, cloud services introduce significant security and privacy challenges when they process sensitive information. For instance, a dataset for machine learning could contain delicate information that traditional encryption approaches cannot protect during data analysis. Homomorphic Encryption (HE) schemes and secure Multi-Party Computation (MPC) are considered solutions for privacy protection in third-party infrastructures. In this paper, we propose a Multi-Cloud Logistic Regression based on Residue Number System (MC-LR-RNS) that provides security, parallel processing, and scalability. To validate the efficiency and practicability of the solution, we provide its analysis with different configurations, datasets, and cloud service providers. We use six available datasets from medicine (diabetes, cancer, drugs, etc.) and genomics. The analysis shows that MC-LR-RNS provides the same levels of quality as non-HE solutions and improved performance due to multi-cloud parallel computations.
format	Статья
author	Babenko, M. G. Бабенко, М. Г.
author_facet	Babenko, M. G. Бабенко, М. Г.
author_sort	Babenko, M. G.
title	Multi-cloud privacy-preserving logistic regression
title_short	Multi-cloud privacy-preserving logistic regression
title_full	Multi-cloud privacy-preserving logistic regression
title_fullStr	Multi-cloud privacy-preserving logistic regression
title_full_unstemmed	Multi-cloud privacy-preserving logistic regression
title_sort	multi-cloud privacy-preserving logistic regression
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2022
url	https://dspace.ncfu.ru/handle/20.500.12258/18617
work_keys_str_mv	AT babenkomg multicloudprivacypreservinglogisticregression AT babenkomg multicloudprivacypreservinglogisticregression
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Multi-cloud privacy-preserving logistic regression

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