dc.contributor.author | Şahinbaş, Kevser | |
dc.contributor.author | Çatak, Ferhat Özgür | |
dc.date.accessioned | 2023-07-27T09:03:05Z | |
dc.date.available | 2023-07-27T09:03:05Z | |
dc.date.issued | 2023 | en_US |
dc.identifier.citation | Şahinbaş, 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 | en_US |
dc.identifier.issn | 2199-1073 | |
dc.identifier.uri | https://dx.doi.org/10.1007/978-3-031-08637-3_3 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12511/11267 | |
dc.description.abstract | Recently, 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer Science and Business Media Deutschland GmbH | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Cognitive Data | en_US |
dc.subject | Cyber Security | en_US |
dc.subject | Data Analysis | en_US |
dc.subject | Healthcare | en_US |
dc.subject | Healthcare Information | en_US |
dc.subject | Internet of Things | en_US |
dc.subject | Interpretable Artificial Intelligence | en_US |
dc.subject | Medical Data | en_US |
dc.subject | Multi-Party Computation | en_US |
dc.subject | Personal Data | en_US |
dc.subject | Privacy-Preserving | en_US |
dc.subject | Secure Computation | en_US |
dc.subject | Security | en_US |
dc.subject | Sensitive Data | en_US |
dc.title | Secure multi-party computation-based privacy-preserving data analysis in healthcare IoT systems | en_US |
dc.type | bookPart | en_US |
dc.relation.ispartof | Internet of Things | en_US |
dc.department | İstanbul Medipol Üniversitesi, İşletme ve Yönetim Bilimleri Fakültesi, Yönetim Bilişim Sistemleri Bölümü | en_US |
dc.authorid | 0000-0002-8076-3678 | en_US |
dc.identifier.volume | Part F739 | en_US |
dc.identifier.startpage | 57 | en_US |
dc.identifier.endpage | 72 | en_US |
dc.relation.publicationcategory | Kitap Bölümü - Uluslararası | en_US |
dc.identifier.doi | 10.1007/978-3-031-08637-3_3 | en_US |
dc.institutionauthor | Şahinbaş, Kevser | |
dc.identifier.scopus | 2-s2.0-85163835660 | en_US |
dc.identifier.scopusquality | Q4 | en_US |