Privacy‐preserving evaluation for support vector clustering

Abstract The authors proposed a privacy‐preserving evaluation algorithm for support vector clustering with a fully homomorphic encryption. The proposed method assigns clustering labels to encrypted test data with an encrypted support function. This method inherits the advantageous properties of supp...

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Autores principales:	J. Byun, J. Lee, S. Park
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5d416729c1f94f82a017a07e80e0162e
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spelling	oai:doaj.org-article:5d416729c1f94f82a017a07e80e0162e2021-11-16T10:15:44ZPrivacy‐preserving evaluation for support vector clustering1350-911X0013-519410.1049/ell2.12047https://doaj.org/article/5d416729c1f94f82a017a07e80e0162e2021-01-01T00:00:00Zhttps://doi.org/10.1049/ell2.12047https://doaj.org/toc/0013-5194https://doaj.org/toc/1350-911XAbstract The authors proposed a privacy‐preserving evaluation algorithm for support vector clustering with a fully homomorphic encryption. The proposed method assigns clustering labels to encrypted test data with an encrypted support function. This method inherits the advantageous properties of support vector clustering, which is naturally inductive to cluster new test data from complex distributions. The authors efficiently implemented the proposed method with elaborate packing of the plaintexts and avoiding non‐polynomial operations that are not friendly to homomorphic encryption. These experimental results showed that the proposed model is effective in terms of clustering performance and has robustness against the error that occurs from homomorphic evaluation and approximate operations.J. ByunJ. LeeS. ParkWileyarticleElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElectronics Letters, Vol 57, Iss 2, Pp 61-64 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electrical engineering. Electronics. Nuclear engineering TK1-9971 J. Byun J. Lee S. Park Privacy‐preserving evaluation for support vector clustering
description	Abstract The authors proposed a privacy‐preserving evaluation algorithm for support vector clustering with a fully homomorphic encryption. The proposed method assigns clustering labels to encrypted test data with an encrypted support function. This method inherits the advantageous properties of support vector clustering, which is naturally inductive to cluster new test data from complex distributions. The authors efficiently implemented the proposed method with elaborate packing of the plaintexts and avoiding non‐polynomial operations that are not friendly to homomorphic encryption. These experimental results showed that the proposed model is effective in terms of clustering performance and has robustness against the error that occurs from homomorphic evaluation and approximate operations.
format	article
author	J. Byun J. Lee S. Park
author_facet	J. Byun J. Lee S. Park
author_sort	J. Byun
title	Privacy‐preserving evaluation for support vector clustering
title_short	Privacy‐preserving evaluation for support vector clustering
title_full	Privacy‐preserving evaluation for support vector clustering
title_fullStr	Privacy‐preserving evaluation for support vector clustering
title_full_unstemmed	Privacy‐preserving evaluation for support vector clustering
title_sort	privacy‐preserving evaluation for support vector clustering
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/5d416729c1f94f82a017a07e80e0162e
work_keys_str_mv	AT jbyun privacypreservingevaluationforsupportvectorclustering AT jlee privacypreservingevaluationforsupportvectorclustering AT spark privacypreservingevaluationforsupportvectorclustering
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Privacy‐preserving evaluation for support vector clustering

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