An Enhanced Differential Homomorphic Model using N-Prime Scheme for Privacy Preservation

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dc.contributor.authorSanni, O. Hafiz
dc.contributor.authorIsiaka, M. Rafiu
dc.contributor.authorBabatunde, N. Akinbowale
dc.contributor.authorJimoh, K. Muhammed
dc.date.accessioned2026-05-16T19:49:07Z
dc.date.available2026-05-16T19:49:07Z
dc.date.issued2023-03-20
dc.description.abstractIn preserving individual privacy in data publishing, several efforts have been made by scholars globally to develop an individual privacy-preserving model and hybridized models which harness the strength of the individual model to increase privacy preservation in data Publishing (PPDP). The Differential homomorphic model (DHM) was among the hybridized models developed that combine differential and homomorphic models. Though is one of the state-of-the-art hybridization methods for privacy preservation because of the Differential model and Homomorphic model strengths of the two hybridized models which are the ability to prevent composition problems and database attacks respectively. However, applying this model is challenging because of the high computational complexities due to the modular exponentiation problem available in the pailler encryption scheme used in DHM. In this research, an N-PRIME homomorphic encryption scheme was proposed to replace the Pailler encryption scheme in the differential homomorphic model (DHM). The designed model was 51% faster than the existing model (Differential Homomorphic Model) in terms of computation time and 48.5% faster when generating the graphical data set, though the designed model consumed 4% more storage space than the existing model.
dc.identifier.issn2579-0617
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/7248
dc.language.isoen
dc.publisherFUOYE Journal of Engineering and Technology
dc.titleAn Enhanced Differential Homomorphic Model using N-Prime Scheme for Privacy Preservation
dc.typeArticle
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