A Constant Time Secure and Private Evaluation of Decision Trees in Smart Cities Enabled by Mobile IoT

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dc.contributor.authorKjamilji, Artrim
dc.date.accessioned2023-11-08T07:32:50Z
dc.date.available2023-11-08T07:32:50Z
dc.date.issued2023en_US
dc.departmentMeslek Yüksekokulları, Meslek Yüksek Okulu, Bilgisayar Teknolojileri Bölümüen_US
dc.description.abstractA server has an already trained decision tree machine learning model and one or more clients have unclassified query(ies) that they wish to classify using the server's model under strict security, privacy, and efficiency constraints. To do so, already existing secure building blocks are used, improved, and adjusted to fit this scenario. On top of the proposed building blocks, novel secure and private Decision Tree Evaluation (sDTE) algorithms are proposed. The proposed building blocks show better performances than the related ones in literature in terms of computation and communication costs. Consequently, experimental evaluations over benchmark datasets show that the proposed sDTE algorithms build on top of the proposed blocks, also outperform the state-of-the-art ones in terms of computation and communication costs as well as on security and privacy characteristics. Our theoretical analysis shows that if the whole decision tree can fit in a single ciphertext, which in the proposed sDTE algorithms is almost always the case, then private tree evaluations are done in constant time and do not depend on the tree depth. To the best of the author's knowledge, this is the first scheme in literature with such properties.en_US
dc.identifier.doi10.1109/SM57895.2023.10112275en_US
dc.identifier.endpage58en_US
dc.identifier.scopus2-s2.0-85159850163en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage51en_US
dc.identifier.urihttps://hdl.handle.net/11467/6937
dc.identifier.urihttps://doi.org/10.1109/SM57895.2023.10112275
dc.identifier.wosWOS:000990489700010en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 IEEE International Conference on Smart Mobility, SM 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectsecure IoT, machine learning, decision trees, classification, homomorphic encryption, privacy preserving algorithmsen_US
dc.titleA Constant Time Secure and Private Evaluation of Decision Trees in Smart Cities Enabled by Mobile IoTen_US
dc.typeConference Objecten_US

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