## 4 Experiments
### Performance Analysis  
We sought to understand the factors influencing the time consumption of UP-SDCG. To investigate this, we analyzed six key variables, specifically row count, column count, sensitive column count, non-sensitive column count, sensitive data percentage, and the computational time taken by the test dataset. These variables are detailed in Table 9. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Precision** & **Recall** & **F1-Score** \\ \hline Customer Information & 100.00\% & 98.36\% & 99.17\% \\ Service data & 96.05\% & 93.59\% & 94.81\% \\ Operation management & 91.67\% & 92.31\% & 91.99\% \\ Financial Supervision & 100.00\% & 100.00\% & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental effects of classification and grading. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Rows** & **Columns** & **Sensitive Columns** & **Non-Sensitive Columns** & **Sensitive Ratio** & **Time (s)** \\ \hline Personnel Information1 & 9 & 50 & 6 & 44 & 0.120 & 6.351 \\ Personnel Information2 & 11,280 & 25 & 6 & 19 & 0.240 & 5.597 \\ Personnel Information3 & 11,329 & 36 & 8 & 28 & 0.222 & 4.825 \\ Contract Information1 & 4372 & 9 & 3 & 6 & 0.333 & 2.025 \\ Contract Information2 & 1979 & 28 & 12 & 16 & 0.429 & 4.269 \\ Project Information1 & 20,206 & 151 & 10 & 141 & 0.066 & 49.493 \\ Project Information2 & 3002 & 150 & 28 & 122 & 0.187 & 29.860 \\ Bank Data1.csv & 48 & 132 & 111 & 21 & 0.841 & 6.675 \\... &... &... &... &... &... &... \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Time consumption examples of the UP-SDCG Model on partial datasets. Initially, we utilized the Pearson correlation coefficient to quantify the linear relationship between variables. The Pearson correlation coefficient is computed using the following formula:  
\[\rho_{X,Y}=\frac{cov(X,Y)}{\sigma_{X}\sigma_{Y}}=\frac{E[(X-\mu_{X})(Y-\mu_{Y})]} {\sigma_{X}\sigma_{Y}} \tag{16}\]  
The resulting correlation values for pairwise variables are computed and presented in Table 10.  
The strength of the correlation between variables can be determined by the magnitude of the correlation coefficient \(\rho\). When \(|\rho|>0.8\), it signifies a strong correlation, while \(0.5\leq|\rho|<0.8\) indicates a moderate correlation. For \(|\rho|\) values falling within \(0.3\leq|\rho|<0.5\), the correlation is considered weak, and if \(|\rho|<0.3\), the variables are essentially uncorrelated. Analyzing Table 10, we observe that model execution time exhibits a strong correlation with the number of non-sensitive columns, a moderate correlation with the number of data columns, a weak correlation with the percentage of data sensitivity, and a negligible correlation with the number of data rows and sensitive columns. Although both the count of non-sensitive columns and the number of data columns influence the model's runtime, their correlation coefficient stands at 0.83449, signifying a strong linear correlation. In this context, either one of these factors could be selected for analysis. However, it is important to note that the Pearson correlation coefficient solely addresses linear correlations between variables. Our comprehensive analysis is extended further in Figure 13.  
Upon analyzing Figure 13, it became evident that a curvilinear relationship exists between the model's elapsed time and the data sensitivity ratio, represented by the equation \(y=\frac{1}{x}\). Consequently, we performed the reciprocal of the sensitivity ratio to derive the column \(\frac{1}{sensitivity\_ratio}\). Subsequently, we recalculated Pearson's correlation coefficient with the elapsed time, yielding the results presented in the updated Table 11:  
Through fitting the execution time, we obtained the fitted equation:  
\[Time=-0.578811+0.263136\cdot insensitivity\_column+0.419442/sensitivity\_ratio \tag{18}\]  
The _R-squared_ value of this fitted equation is 0.895, and the \(p\)-value is \(3.02\times 10^{-48}\), indicating a favorable fit.