A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption

Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data rema...
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Autor*in:	Mikhail Babenko [verfasserIn] Elena Golimblevskaia [verfasserIn] Andrei Tchernykh [verfasserIn] Egor Shiriaev [verfasserIn] Tatiana Ermakova [verfasserIn] Luis Bernardo Pulido-Gaytan [verfasserIn] Georgii Valuev [verfasserIn] Arutyun Avetisyan [verfasserIn] Lana A. Gagloeva [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	CKKS homomorphic encryption homomorphic encryption standard matrix multiplication PALISADE SEAL

Übergeordnetes Werk:	In: Big Data and Cognitive Computing - MDPI AG, 2018, 7(2023), 2, p 84
Übergeordnetes Werk:	volume:7 ; year:2023 ; number:2, p 84

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/bdcc7020084

Katalog-ID:	DOAJ094208832

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allfieldsSound	10.3390/bdcc7020084 doi (DE-627)DOAJ094208832 (DE-599)DOAJ7e4539b70412475087d12613715de42e DE-627 ger DE-627 rakwb eng Mikhail Babenko verfasserin aut A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data remains a challenge. The computational complexity of some encrypted operations can significantly increase processing time. In this paper, we focus on the analysis of two state-of-the-art HE matrix multiplication algorithms with the best time and space complexities. We show how their performance depends on the libraries and the execution context, considering the standard Cheon–Kim–Kim–Song (CKKS) HE scheme with fixed-point numbers based on the Microsoft SEAL and PALISADE libraries. We show that Windows OS for the SEAL library and Linux OS for the PALISADE library are the best options. In general, PALISADE-Linux outperforms PALISADE-Windows, SEAL-Linux, and SEAL-Windows by 1.28, 1.59, and 1.67 times on average for different matrix sizes, respectively. We derive high-precision extrapolation formulas to estimate the processing time of HE multiplication of larger matrices. CKKS homomorphic encryption homomorphic encryption standard matrix multiplication PALISADE SEAL Technology T Elena Golimblevskaia verfasserin aut Andrei Tchernykh verfasserin aut Egor Shiriaev verfasserin aut Tatiana Ermakova verfasserin aut Luis Bernardo Pulido-Gaytan verfasserin aut Georgii Valuev verfasserin aut Arutyun Avetisyan verfasserin aut Lana A. Gagloeva verfasserin aut In Big Data and Cognitive Computing MDPI AG, 2018 7(2023), 2, p 84 (DE-627)888151454 (DE-600)2895385-X 25042289 nnns volume:7 year:2023 number:2, p 84 https://doi.org/10.3390/bdcc7020084 kostenfrei https://doaj.org/article/7e4539b70412475087d12613715de42e kostenfrei https://www.mdpi.com/2504-2289/7/2/84 kostenfrei https://doaj.org/toc/2504-2289 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2023 2, p 84
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author	Mikhail Babenko
spellingShingle	Mikhail Babenko misc CKKS misc homomorphic encryption misc homomorphic encryption standard misc matrix multiplication misc PALISADE misc SEAL misc Technology misc T A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption
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topic_title	A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption CKKS homomorphic encryption homomorphic encryption standard matrix multiplication PALISADE SEAL
topic	misc CKKS misc homomorphic encryption misc homomorphic encryption standard misc matrix multiplication misc PALISADE misc SEAL misc Technology misc T
topic_unstemmed	misc CKKS misc homomorphic encryption misc homomorphic encryption standard misc matrix multiplication misc PALISADE misc SEAL misc Technology misc T
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title	A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption
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title_sort	comparative study of secure outsourced matrix multiplication based on homomorphic encryption
title_auth	A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption
abstract	Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data remains a challenge. The computational complexity of some encrypted operations can significantly increase processing time. In this paper, we focus on the analysis of two state-of-the-art HE matrix multiplication algorithms with the best time and space complexities. We show how their performance depends on the libraries and the execution context, considering the standard Cheon–Kim–Kim–Song (CKKS) HE scheme with fixed-point numbers based on the Microsoft SEAL and PALISADE libraries. We show that Windows OS for the SEAL library and Linux OS for the PALISADE library are the best options. In general, PALISADE-Linux outperforms PALISADE-Windows, SEAL-Linux, and SEAL-Windows by 1.28, 1.59, and 1.67 times on average for different matrix sizes, respectively. We derive high-precision extrapolation formulas to estimate the processing time of HE multiplication of larger matrices.
abstractGer	Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data remains a challenge. The computational complexity of some encrypted operations can significantly increase processing time. In this paper, we focus on the analysis of two state-of-the-art HE matrix multiplication algorithms with the best time and space complexities. We show how their performance depends on the libraries and the execution context, considering the standard Cheon–Kim–Kim–Song (CKKS) HE scheme with fixed-point numbers based on the Microsoft SEAL and PALISADE libraries. We show that Windows OS for the SEAL library and Linux OS for the PALISADE library are the best options. In general, PALISADE-Linux outperforms PALISADE-Windows, SEAL-Linux, and SEAL-Windows by 1.28, 1.59, and 1.67 times on average for different matrix sizes, respectively. We derive high-precision extrapolation formulas to estimate the processing time of HE multiplication of larger matrices.
abstract_unstemmed	Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data remains a challenge. The computational complexity of some encrypted operations can significantly increase processing time. In this paper, we focus on the analysis of two state-of-the-art HE matrix multiplication algorithms with the best time and space complexities. We show how their performance depends on the libraries and the execution context, considering the standard Cheon–Kim–Kim–Song (CKKS) HE scheme with fixed-point numbers based on the Microsoft SEAL and PALISADE libraries. We show that Windows OS for the SEAL library and Linux OS for the PALISADE library are the best options. In general, PALISADE-Linux outperforms PALISADE-Windows, SEAL-Linux, and SEAL-Windows by 1.28, 1.59, and 1.67 times on average for different matrix sizes, respectively. We derive high-precision extrapolation formulas to estimate the processing time of HE multiplication of larger matrices.
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title_short	A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption
url	https://doi.org/10.3390/bdcc7020084 https://doaj.org/article/7e4539b70412475087d12613715de42e https://www.mdpi.com/2504-2289/7/2/84 https://doaj.org/toc/2504-2289
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A Comparative Study of Secure Outsourced Matrix Multiplication Based on Homomorphic Encryption

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