Efficient estimation of Sigmoid and Tanh activation functions for homomorphically encrypted data using Artificial Neural Networks

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dc.authorid0009-0001-4214-8738
dc.authorid0000-0003-2865-6370
dc.contributor.authorHarb, Mhd Raja Abouen_US
dc.contributor.authorÇeliktaş, Barışen_US
dc.date.accessioned2025-08-18T06:51:44Z
dc.date.available2025-08-18T06:51:44Z
dc.date.issued2024
dc.departmentIşık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Bilgisayar Mühendisliği Yüksek Lisans Programıen_US
dc.departmentIşık University, School of Graduate Studies, Master’s Program in Computer Engineeringen_US
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractThis paper presents a novel approach to estimating Sigmoid and Tanh activation functions using Artificial Neural Networks (ANN) optimized for homomorphic encryption. The proposed method is compared against second-degree polynomial and Piecewise Linear approximations, demonstrating a minor loss in accuracy while maintaining computational efficiency. Our results suggest that the ANN-based estimator is a viable alternative for secure machine learning models requiring privacypreserving computation.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationHarb, M. R. A. & Çeliktaş, B. (2024). Efficient estimation of Sigmoid and Tanh activation functions for homomorphically encrypted data using Artificial Neural Networks. Paper presented at the 8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024 - Proceedings. doi:10.1109/ISAS64331.2024.10845450en_US
dc.identifier.doi10.1109/ISAS64331.2024.10845450
dc.identifier.isbn9798331540104
dc.identifier.scopus2-s2.0-85218035349
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/11729/6619
dc.identifier.urihttps://doi.org/10.1109/ISAS64331.2024.10845450
dc.indekslendigikaynakScopusen_US
dc.institutionauthorHarb, Mhd Raja Abouen_US
dc.institutionauthorÇeliktaş, Barışen_US
dc.institutionauthorid0009-0001-4214-8738
dc.institutionauthorid0000-0003-2865-6370
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrencien_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectActivation function estimationen_US
dc.subjectArtificial Neural Networks (ANN)en_US
dc.subjectHomomorphic encryptionen_US
dc.subjectPrivacy-preserving machine learningen_US
dc.subjectActivation functionsen_US
dc.subjectFunction estimationen_US
dc.subjectHo-momorphic encryptionsen_US
dc.subjectHomomorphic-encryptionsen_US
dc.subjectMachine-learningen_US
dc.subjectNeural-networksen_US
dc.subjectPrivacy preservingen_US
dc.subjectDifferential privacyen_US
dc.titleEfficient estimation of Sigmoid and Tanh activation functions for homomorphically encrypted data using Artificial Neural Networksen_US
dc.typeConference Objecten_US
dspace.entity.typePublicationen_US

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