# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 3 Experiment
### Experimental Design and Result Analysis
#### 3.2.3 Experimental Validation of the Effectiveness of Clustering-Based Dynamic Classification  
Similarly, to assess the effectiveness of our proposed clustering-based dynamic classification strategy, we carried out a comparative experiment on the dataset of Table 4. To ensure a controlled single-variable comparison, all experiments were conducted using the same voting strategy and fine-grained division approach. Table 9 compares the performance of the dynamic classification strategy and the static classification strategy across the four systems. As shown, each method exhibits advantages and disadvantages depending on the specific system. The static classification strategy is more suitable for Systems 1 and 3, while the dynamic strategy performs better in Systems 2 and 4. However, it is important to note that the static classification results are based on carefully tuned threshold values, while the dynamic classification strategy operates without the need for manual adjustment, offering greater adaptability and scalability in practical applications. In addition, in System 2, the static classification strategy achieved an accuracy of only 45.41%, whereas the implementation of the dynamic classification strategy resulted in a significantly higher accuracy of 73.47%, representing an improvement of 28.06 percentage points. Despite a decline in accuracy for System 1 and System 3, the dynamic strategy consistently achieves over 58% accuracy, demonstrating its robustness across different systems. Therefore, the dynamic classification strategy demonstrates greater overall advantages and is better suited for practical applications. To clearly demonstrate how attribute sensitivity is used for classification, we use data sheet \"big_view_szoptimizeintersect\" from System 3 as a representative example. It contains 11 attributes, which were finally classified as six Level 1 (non-sensitive), three Level 2 (weakly sensitive), and two Level 3 (strongly sensitive) sensitivity attributes. The detailed sensitivity value and their associated classification results are provided in Table 10.  
It should be noted that the attribute \"insectgdtype\" was classified as non-sensitive (Level 1) based on clustering analysis and association rule mining. The attribute \"insectgdtype\", which denotes the type of intersecting work point, is considered non-sensitive in \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **System1** & **System2** & **System3** & **System4** \\ \hline Canonical and semi-canonical data & **58.33\%** & **73.47\%** & **58.51\%** & **57.80\%** \\ Structured data & 57.41\% & 69.39\% & 55.32\% & **57.80\%** \\ Effect & \(\uparrow\) 0.92\% & \(\uparrow\) 4.08\% & \(\uparrow\) 3.19\% & 0.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results of fine-grained data division. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **System1** & **System2** & **System3** & **System4** \\ \hline Dynamic classification strategy & 58.33\% & **73.47\%** & 58.51\% & **57.80\%** \\ Static classification strategy & **66.67\%** & 45.41\% & **72.34\%** & 49.13\% \\ Effect & \(\downarrow\) 7.84\% & \(\uparrow\) 28.06\% & \(\downarrow\) 13.83\% & \(\uparrow\) 8.67\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Effectiveness of combined dynamic and static classification strategies. the railway domain. Our method accurately identifies and classifies it as a Level 1 sensitive attribute. Therefore, only the remaining 10 attributes were subjected to clustering-based dynamic classification and majority voting to determine the final sensitivity levels. The results of this process yielded seven Level 3, three Level 2, and one Level 1 sensitivity classifications. Subsequently, according to the expert-provided classification rules, all attributes containing \"id\" and \"date\" were reclassified as Level 1.