Bibliometrics of Machine Learning Research Using Homomorphic Encryption

Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and und...

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Autores principales:	Zhigang Chen, Gang Hu, Mengce Zheng, Xinxia Song, Liqun Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	homomorphic encryption machining learning privacy security bibliometrics Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/e93f3cd955ef4619bf3db38af4483c7b
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spelling	oai:doaj.org-article:e93f3cd955ef4619bf3db38af4483c7b2021-11-11T18:19:46ZBibliometrics of Machine Learning Research Using Homomorphic Encryption10.3390/math92127922227-7390https://doaj.org/article/e93f3cd955ef4619bf3db38af4483c7b2021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2792https://doaj.org/toc/2227-7390Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach.Zhigang ChenGang HuMengce ZhengXinxia SongLiqun ChenMDPI AGarticlehomomorphic encryptionmachining learningprivacysecuritybibliometricsMathematicsQA1-939ENMathematics, Vol 9, Iss 2792, p 2792 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	homomorphic encryption machining learning privacy security bibliometrics Mathematics QA1-939
spellingShingle	homomorphic encryption machining learning privacy security bibliometrics Mathematics QA1-939 Zhigang Chen Gang Hu Mengce Zheng Xinxia Song Liqun Chen Bibliometrics of Machine Learning Research Using Homomorphic Encryption
description	Since the first fully homomorphic encryption scheme was published in 2009, many papers have been published on fully homomorphic encryption and its applications. Machine learning is one of the most interesting applications and has drawn a lot of attention from researchers. To better represent and understand the field of Homomorphic Encryption in Machine Learning (HEML), this paper utilizes automated citation and topic analysis to characterize the HEML research literature over the years and provide the bibliometrics assessments for this burgeoning field. This is conducted by using a bibliometric statistical analysis approach. We make use of web-based literature databases and automated tools to present the development of HEML. This allows us to target several popular topics for in-depth discussion. To achieve these goals, we have chosen the well-established Scopus literature database and analyzed them through keyword counts and Scopus relevance searches. The results show a relative increase in the number of papers published each year that involve both homomorphic cryptography and machine learning. Using text mining of articles titles, we have found that cloud computing is a popular topic in this field, which also includes neural networks, big data, and the Internet of Things. The analysis results show that China, the US, and India have generated almost half of all the research contributions in HEML. The citation statistics, keyword statistics, and topic analyses give us a quick overview of the development of the field, which can be of great help to new researchers. It is also possible to apply our methodology to other research areas, and we see great value in this approach.
format	article
author	Zhigang Chen Gang Hu Mengce Zheng Xinxia Song Liqun Chen
author_facet	Zhigang Chen Gang Hu Mengce Zheng Xinxia Song Liqun Chen
author_sort	Zhigang Chen
title	Bibliometrics of Machine Learning Research Using Homomorphic Encryption
title_short	Bibliometrics of Machine Learning Research Using Homomorphic Encryption
title_full	Bibliometrics of Machine Learning Research Using Homomorphic Encryption
title_fullStr	Bibliometrics of Machine Learning Research Using Homomorphic Encryption
title_full_unstemmed	Bibliometrics of Machine Learning Research Using Homomorphic Encryption
title_sort	bibliometrics of machine learning research using homomorphic encryption
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e93f3cd955ef4619bf3db38af4483c7b
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_version_	1718431917153452032

Bibliometrics of Machine Learning Research Using Homomorphic Encryption

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