An intelligent atrous convolution-based cascaded deep learning framework for enhanced privacy preservation performance in edge computing

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dc.contributor.authorSiryeh, Fatima Abu
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.date.accessioned2025-08-14T17:53:29Z
dc.date.available2025-08-14T17:53:29Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.description.abstractA system without any communication delays, called edge computing, has been introduced for nearer and faster services. The major concern in the edge computing scenario is its privacy risks. A user, as well as a cloud data preservation scheme, is the main aim of this paperwork. Test data is given by the user to access the cloud-based data processing framework. The training of the suitable model is carried out by utilizing the data stored in the cloud. The suggested model divides the entire model into two sections, namely, the untrusted cloud and the trusted edge. On the trusted edge side the data is directly provided to the developed advanced deep learning model called the Atrous Convolution based Cascaded Deep Temporal Convolution Network (ACC-DTCN) for the data analysis process. However, instead of giving the whole data directly to the untrusted cloud side, the test data is protected on the cloud side by utilizing a hybrid encryption technique called the Optimal Hybrid Encryption Model (OHEM). Both Attribute-Based Encryption (ABE) and Homomorphic Encryption (HE) are utilized in the recommended OHEM scheme. The OHEM variables are tuned with the help of an advanced algorithm called the Enhanced Ladybug Beetle Optimization algorithm (ELBOA). The confidence score vector among the testing and training data is predicted by the implemented ACC-DTCN model by utilizing the encrypted data on the cloud side. The suggested privacy preservation scheme provides higher prediction accuracy and prevents interference attacks while contrasting it against conventional methods during extensive experimentations.
dc.identifier.citationSiryeh, F. A., & Ibrahim, A. A. (2025). An intelligent atrous convolution-based cascaded deep learning framework for enhanced privacy preservation performance in edge computing. Journal of Ambient Intelligence and Smart Environments, 17(2), 198-229. 10.3233/AIS-230626
dc.identifier.doi10.3233/AIS-230626
dc.identifier.endpage229
dc.identifier.issn1876-1364
dc.identifier.issn1876-1372
dc.identifier.issue2
dc.identifier.startpage198
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5931
dc.identifier.volume17
dc.identifier.wosWOS:001490107200003
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.institutionauthorSiryeh, Fatima Abu
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherIOS Press
dc.relation.ispartofJournal of Ambient Intelligence and Smart Environments
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectInternet of Things
dc.subjectcloud computing
dc.subjectedge computing
dc.subjectprivacy and security of data
dc.subjectAtrous Convolution based Cascaded Deep Temporal Convolution Network
dc.subjectOptimal Hybrid Encryption Model
dc.subjectEnhanced Ladybug Beetle Optimization algorithm
dc.titleAn intelligent atrous convolution-based cascaded deep learning framework for enhanced privacy preservation performance in edge computing
dc.typeArticle

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