Browsing by Author "Babatunde, N. Akinbowale"

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    An Enhanced Differential Homomorphic Model using N-Prime Scheme for Privacy Preservation
    (FUOYE Journal of Engineering and Technology, 2023-03-20) Sanni, O. Hafiz; Isiaka, M. Rafiu; Babatunde, N. Akinbowale; Jimoh, K. Muhammed
    In 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.
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    Implication of Variation in Ant Colony Optimization Algorithm (ACO) Parameters on Feature Subset Size
    (Computing and Information Systems Journal, 2017) Babatunde, Ronke Seyi; Isiaka, Mope Rafiu; Oyeranmi J. Adigun; Babatunde, N. Akinbowale
    Purpose: The purpose of this research is to apply ant colony optimization (ACO), a nature inspired computational (NIC) technique to achieve optimal feature subset selection, for the purpose of data dimensionality reduction, which will remove redundancy, provide a reduced storage space, improve memory utilization and subsequently, classification task. Design/Methodology/Approach: The ACO feature subset selection process was filter-based, accomplished by using correlation coefficient as the heuristic information which guides the search to determine the pixel (feature) attractiveness. The intensity of the pixel represents the pheromone level . Also, ρ, φ, and are key parameters which were tuned to various arbitrary values at each iteration to determine the best combination of parameters which can result in a reduced feature subset (window size). The procedure was simulated in MATLAB 2012. Findings: The study has been able to demonstrate that experimental results obtained by tuning the parameters of ACO at each run gave a reduced feature subset (window size), at parameter settings of α (0.10), β (0.45), ρ (0.50), φ (1.0) which was salient for efficient face recognition thereby demonstrating the effectiveness and superiority of filter-based over wrapper–based feature selection. Research Limitation: The major limiting factor in this research was time and materials which hindered the further testing and implementation of innovative computational intelligence. Originality/Value: This research work is valuable because it gives the implication of the variation of the ACO parameters which determines the extent to which the optimal solution construction that results in a reduced feature subset can be realized.