Secure multi-party computation-based privacy-preserving data analysis in healthcare IoT systems

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dc.authorid0000-0002-8076-3678
dc.contributor.authorŞahinbaş, Kevser
dc.contributor.authorÇatak, Ferhat Özgür
dc.date.accessioned2023-07-27T09:03:05Z
dc.date.available2023-07-27T09:03:05Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü
dc.description.abstractRecently, many innovations have been experienced in healthcare by rapidly growing Internet-of-Things (IoT) technology that provides significant developments and facilities in the health sector and improves daily human life. The IoT bridges people and information technology and speeds up shopping. For these reasons, IoT technology has started to be used on a large scale. Thanks to the use of IoT technology in health services, chronic disease monitoring, health monitoring, rapid intervention, early diagnosis and treatment, etc., facilitate the delivery of health services. However, the data transferred to the digital environment pose a threat of privacy leakage. Unauthorized persons have used them, and there have been malicious attacks on the health and privacy of individuals. In this chapter, it is aimed to propose a model to handle the privacy problems based on federated learning. Besides, we apply secure multi-party computation. Our proposed model presents an extensive privacy and data analysis and achieves high performance.
dc.identifier.citationŞahinbaş, K. ve Çatak, F. Ö. (2023). Secure multi-party computation-based privacy-preserving data analysis in healthcare IoT systems. Internet of Things içinde (57-72. ss.). Springer Science and Business Media Deutschland GmbH. https://dx.doi.org/10.1007/978-3-031-08637-3_3
dc.identifier.doi10.1007/978-3-031-08637-3_3
dc.identifier.endpage72
dc.identifier.issn2199-1073
dc.identifier.scopus2-s2.0-85163835660
dc.identifier.scopusqualityQ4
dc.identifier.startpage57
dc.identifier.urihttps://dx.doi.org/10.1007/978-3-031-08637-3_3
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11267
dc.identifier.volumePart F739
dc.indekslendigikaynakScopus
dc.institutionauthorŞahinbaş, Kevser
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofInternet of Thingsen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectArtificial Intelligence
dc.subjectCognitive Data
dc.subjectCyber Security
dc.subjectData Analysis
dc.subjectHealthcare
dc.subjectHealthcare Information
dc.subjectInternet of Things
dc.subjectInterpretable Artificial Intelligence
dc.subjectMedical Data
dc.subjectMulti-Party Computation
dc.subjectPersonal Data
dc.subjectPrivacy-Preserving
dc.subjectSecure Computation
dc.subjectSecurity
dc.subjectSensitive Data
dc.titleSecure multi-party computation-based privacy-preserving data analysis in healthcare IoT systems
dc.typeBook Chapter

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