Efficient differentially private kernel support vector classifier for multi-class classification
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Jinseong | - |
dc.contributor.author | Byun, Junyoung | - |
dc.contributor.author | Choi, Yujin | - |
dc.contributor.author | Lee, Jaewook | - |
dc.contributor.author | Park, Saerom | - |
dc.date.accessioned | 2024-02-14T01:00:35Z | - |
dc.date.available | 2024-02-14T01:00:35Z | - |
dc.date.issued | 2023-01 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.issn | 1872-6291 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/71995 | - |
dc.description.abstract | In this paper, we propose a multi-class classification method using kernel supports and a dynamical system under differential privacy. For small datasets, kernel methods, such as kernel support vector machines (SVMs), show good generalization performance with high-dimensional feature mapping. However, kernel SVMs have a fundamental weakness in achieving differential privacy because they construct decision functions based on a subset of the training data called support vectors. Furthermore, multi-class SVMs must decompose the training data into a binary class, which requires multiple accesses to the same training data. To address these limitations, we develop a two-phase classification algorithm based on support vector data description (SVDD). We first generate and prove a differentially private SVDD (DP-SVDD) by perturbing the sphere center in a high-dimensional feature space. Next, we partition the input space using a dynamical system and classify the partitioned regions using a noisy count. The proposed method results in robust, fast, and user-friendly multi-class classification, even on small-sized datasets, where differential privacy performs poorly. | - |
dc.format.extent | 19 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.title | Efficient differentially private kernel support vector classifier for multi-class classification | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.ins.2022.10.075 | - |
dc.identifier.bibliographicCitation | INFORMATION SCIENCES, v.619, pp 889 - 907 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000908349500011 | - |
dc.identifier.scopusid | 2-s2.0-85142825611 | - |
dc.citation.endPage | 907 | - |
dc.citation.startPage | 889 | - |
dc.citation.title | INFORMATION SCIENCES | - |
dc.citation.volume | 619 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Differential privacy | - |
dc.subject.keywordAuthor | Kernel method | - |
dc.subject.keywordAuthor | Support vector data description | - |
dc.subject.keywordAuthor | Support vector machine | - |
dc.subject.keywordPlus | MACHINE | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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