Privacy‐preserving evaluation for support vector clustering

Autor:	J. Byun, J. Lee, S. Park
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Data security Algebra Support vector machines Electrical engineering. Electronics. Nuclear engineering TK1-9971
Zdroj:	Electronics Letters, Vol 57, Iss 2, Pp 61-64 (2021)
Druh dokumentu:	article
ISSN:	1350-911X 0013-5194
DOI:	10.1049/ell2.12047
Popis:	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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/5d416729c1f94f82a017a07e80e0162e Zobrazit plný text záznamu Plný text View record in DOAJ