Non-cryptographic privacy preserving machine learning methods: a review

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dc.authorid0000-0002-8076-3678
dc.contributor.authorŞahinbaş, Kevser
dc.contributor.authorÇatak, Ferhat Özgür
dc.contributor.authorKuzlu, Murat
dc.contributor.authorTabassum, Maliha
dc.contributor.authorSarp, Salih
dc.date.accessioned2024-01-16T10:04:07Z
dc.date.available2024-01-16T10:04:07Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractIn recent years, the use of Machine Learning (ML) techniques to exploit data and produce predictive models has become widespread in decision-making and problem-solving across various fields, including healthcare, energy, retail, transportation, and many more. Generally, a well-performing ML model requires large volumes of training data. However, collecting data and using it to predict behavior poses significant challenges to the privacy of individuals and organizations, such as data breaches, loss of privacy, and corresponding financial damage. Therefore, well-designed privacy-preserving ML (PPML) methods are significantly required for many emerging applications to mitigate these problems. This paper provides a comprehensive review of non-cryptographic privacy-preserving ML along with selected methods, such as differential privacy and federated learning. This paper aims to provide a roadmap for future research directions in the PPML field.
dc.identifier.citationŞahinbaş, K., Çatak, F. Ö., Kuzlu, M., Tabassum, M. ve Sarp, S. (2024). Non-cryptographic privacy preserving machine learning methods: a review. 2nd International Conference on Advanced Engineering, Technology and Applications, ICAETA içinde (410-421. ss.). Istanbul, 10-11 March 2023. https://dx.doi.org/10.1007/978-3-031-50920-9_32
dc.identifier.doi10.1007/978-3-031-50920-9_32
dc.identifier.endpage421
dc.identifier.isbn9783031509193
dc.identifier.issn1865-0929
dc.identifier.scopus2-s2.0-85180780438
dc.identifier.scopusqualityQ4
dc.identifier.startpage410
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-031-50920-9_32
dc.identifier.urihttps://hdl.handle.net/20.500.12511/12149
dc.identifier.volume1983
dc.indekslendigikaynakScopus
dc.institutionauthorŞahinbaş, Kevser
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartof2nd International Conference on Advanced Engineering, Technology and Applications, ICAETAen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFederated Learning
dc.subjectMachine Learning
dc.subjectPrivacy-Preserving
dc.titleNon-cryptographic privacy preserving machine learning methods: a review
dc.typeConference Object

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