Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic e...

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Autores principales:	Jiasen Liu, Chao Wang, Zheng Tu, Xu An Wang, Chuan Lin, Zhihu Li
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Technology (General) T1-995 Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/8f9ff17ce650484abc304ff44fcb104f
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spelling	oai:doaj.org-article:8f9ff17ce650484abc304ff44fcb104f2021-11-15T01:19:57ZSecure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace1939-012210.1155/2021/8759922https://doaj.org/article/8f9ff17ce650484abc304ff44fcb104f2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8759922https://doaj.org/toc/1939-0122With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.Jiasen LiuChao WangZheng TuXu An WangChuan LinZhihu LiHindawi-WileyarticleTechnology (General)T1-995Science (General)Q1-390ENSecurity and Communication Networks, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology (General) T1-995 Science (General) Q1-390
spellingShingle	Technology (General) T1-995 Science (General) Q1-390 Jiasen Liu Chao Wang Zheng Tu Xu An Wang Chuan Lin Zhihu Li Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace
description	With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.
format	article
author	Jiasen Liu Chao Wang Zheng Tu Xu An Wang Chuan Lin Zhihu Li
author_facet	Jiasen Liu Chao Wang Zheng Tu Xu An Wang Chuan Lin Zhihu Li
author_sort	Jiasen Liu
title	Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace
title_short	Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace
title_full	Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace
title_fullStr	Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace
title_full_unstemmed	Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace
title_sort	secure knn classification scheme based on homomorphic encryption for cyberspace
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/8f9ff17ce650484abc304ff44fcb104f
work_keys_str_mv	AT jiasenliu secureknnclassificationschemebasedonhomomorphicencryptionforcyberspace AT chaowang secureknnclassificationschemebasedonhomomorphicencryptionforcyberspace AT zhengtu secureknnclassificationschemebasedonhomomorphicencryptionforcyberspace AT xuanwang secureknnclassificationschemebasedonhomomorphicencryptionforcyberspace AT chuanlin secureknnclassificationschemebasedonhomomorphicencryptionforcyberspace AT zhihuli secureknnclassificationschemebasedonhomomorphicencryptionforcyberspace
_version_	1718428936627552256

Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

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