## 4 Experiments
### Comparative Analysis
#### 4.2.2 Experimental Results  
Using the test dataset, we conducted a comprehensive comparison of UP-SDCG with the existing sensitive data recognition models commonly employed in the industry. In the subsequent sections, we elaborate on the specifics of the comparison model and present the results of our experiments. * DSC sensitive data identification model(Alibaba): Including 210 detection rules, the financial classification template in the DSC sensitive data identification model is constructed with reference to the industry standard Financial Data Security Data Security Classification Guide;
* DSGC Sensitive Data Identification Model (Tencent): Use the built-in general classification and grading standard template for identification, which contains 41 detection rules;
* GoDLP (Bytedance): ByteDance's open source tool for sensitive data identification in 2022, which can support structured data and unstructured data identification, with 36 built-in detection rules. UP-SDCG exhibited exceptional accuracy, surpassing all other three comparison models with a remarkable accuracy rate of over 95% on all three datasets, as depicted in Figure 10. Notably, the DSC achieved high accuracy in recognizing personnel information, boasting a perfect 100% accuracy rate for both personnel information and project information. However, its performance in detecting contract dates was subpar, attributed to the complexity of contract data that often contain various types of date information, such as contract start and end dates. DSC's limitations lie in its inability to correctly classify the granularity of the date categories, resulting in insufficient delineation ability. In contrast, our model demonstrated fine-grained category classification through the utilization of keyword augmentation techniques, leading to a significant improvement in recognition accuracy. By effectively recognizing and classifying sensitive data, including numerical information like employees' work numbers and identity IDs, our model outperformed DSGC, which has a high misclassification rate for such data. Furthermore, while GoDLP achieved a higher accuracy rate by adhering to stricter rules, it recognized fewer sensitive data instances. Figure 9: Statistical analysis of sensitive data distribution in simulation dataset. UP-SDCG demonstrated a remarkable recall rate of over 94% across all test sets, resulting in fewer omissions, as depicted in Figure 11. By formulating more than 1100 detection rules based on industry standards, UP-SDCG covered a broader range of sensitive data compared to other three comparison models. As a result, its recall rate exhibited significant improvement. A comparison with models DSGC and GoDLP, which utilize generic sensitive data recognition templates revealed the limitations of current generic data classification and grading models in the financial domain. This highlights the crucial role played by domain-specific detection rules in achieving accurate recognition within the financial context. By considering both false alarms and leakage cases, we demonstrate the superior performance of UP-SDCG over existing industry models, as illustrated in Figure 12. Specifically, when compared to DSC, which also leverages financial hierarchical classification template recognition and detection based on industry standards, our model achieves a lower leakage rate due to its comprehensive detection rules. Additionally, we incorporated keyword augmentation and expansion techniques, enabling fine-grained and accurate hierarchical classification, thus effectively mitigating false alarm situations. Furthermore, a comparison with generalized sensitive data hierarchical classification models, such as  
Figure 11: Comparison of recall in data grading and classification models. Figure 10: Comparison of precision in data grading and classification models. DSGC and GoDLP, further underscores the advantages of our financial data recognition hierarchical classification rule base construction.