Enhancing Gram-Based Fuzzy Keyword Search in Encrypted Data Using XGBoost Comparison to SVM for Increased Accuracy
K.Bhanu sri Harshitha, S. Mahaboob Basha, Bushra Hamid · 2024
This study sets out to explore and compare two widely used techniques, Support Vector Machines (SVM) and Extreme Gradient Boosting (XGBoost), in the context of Gram-Based Fuzzy Keyword Search applied to encrypted data. By leveraging a large dataset consisting of 21,875 samples, the research aims to boost the accuracy of this search method through various training and testing splits. With an average GPower of nearly 85% (with settings of$\alpha=0.05$and power$=0.85$), the study analyzes performance at two significance levels:$\alpha$of 0.05 and 0.85. The findings revealed a$\mathbf{p}$-value of$p=0.664(p>0.05)$, which suggests that there isn't a statistically significant difference between the two approaches. However, when it comes to accuracy, XGBoost takes the lead, achieving an impressive accuracy rate of 90 %, while SVM lags slightly behind at 87 %. In essence, when comparing Gram-Based Fuzzy Keyword Search using XGBoost with the same search using SVM, XGBoost clearly demonstrates its superiority with a 90 % recognition rate. This research not only highlights the effectiveness of XGBoost over SVM but also contributes to the broader goal of enhancing the accuracy of Gram-Based Fuzzy Keyword Search in the context of encrypted data.