Data mining applications in banking sector while preserving customer privacy
| dc.authorid | 0000-0002-5971-9218 | |
| dc.contributor.author | Doğuç, Özge | |
| dc.date.accessioned | 2022-12-16T07:17:34Z | |
| dc.date.available | 2022-12-16T07:17:34Z | |
| dc.date.issued | 2022 | |
| dc.department | İstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü | |
| dc.description.abstract | In real-life data mining applications, organizations cooperate by using each other’s data on the same data mining task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving data mining (PPDM) practices involve rules and techniques that allow parties to collaborate on data mining applications while keeping their data private. The objective of this paper is to present a number of PPDM protocols and show how PPDM can be used in data mining applications in the banking sector. For this purpose, the paper discusses homomorphic cryptosystems and secure multiparty computing. Supported by experimental analysis, the paper demonstrates that data mining tasks such as clustering and Bayesian networks (association rules) that are commonly used in the banking sector can be efficiently and securely performed. This is the first study that combines PPDM protocols with applications for banking data mining. | |
| dc.identifier.citation | Doğuç, Ö. (2022). Data mining applications in banking sector while preserving customer privacy. Emerging Science Journal, 6(6), 1444-1454. https://doi.org/10.28991/ESJ-2022-06-06-014 | |
| dc.identifier.doi | 10.28991/ESJ-2022-06-06-014 | |
| dc.identifier.endpage | 1454 | |
| dc.identifier.issn | 2610-9182 | |
| dc.identifier.issue | 6 | |
| dc.identifier.scopus | 2-s2.0-85143237296 | |
| dc.identifier.scopusquality | Q1 | |
| dc.identifier.startpage | 1444 | |
| dc.identifier.uri | https://doi.org/10.28991/ESJ-2022-06-06-014 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12511/10133 | |
| dc.identifier.volume | 6 | |
| dc.indekslendigikaynak | Scopus | |
| dc.institutionauthor | Doğuç, Özge | |
| dc.language.iso | en | |
| dc.publisher | Ital Publication | |
| dc.relation.ispartof | Emerging Science Journal | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Banking Processes | |
| dc.subject | Data Management | |
| dc.subject | Data Mining | |
| dc.subject | Data Security | |
| dc.title | Data mining applications in banking sector while preserving customer privacy | |
| dc.type | Article |
