Open AccessPrivacy‐preserving evaluation for support vector clustering
Author(s) -
Byun J.,
Lee J.,
Park S.
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12047
Subject(s) - homomorphic encryption , cluster analysis , computer science , robustness (evolution) , encryption , data mining , cure data clustering algorithm , correlation clustering , algorithm , theoretical computer science , artificial intelligence , biochemistry , chemistry , gene , operating system
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.