# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
## 4 Discussion  
In Section 3.2, we conducted four experiments. The results of the four experiments validate the effectiveness of each individual identification and classification module proposed for structured sensitive data. Experimental results show that the proposed method achieves an average accuracy of 62.03% across the four systems. In addition, we have developed a domain-specific rule base for the railway domain to assist in the identification of classification-sensitive attributes. The rule base consists of two components: identification rules and classification rules. Identification rules are employed to facilitate the identification of semi-canonical sensitive data. As shown in Experiment 2 (Section 3.2.2), the use of identification rules combined with fine-grained division for identifying semi-canonical data yields an average accuracy improvement of 2.05%, validating the effectiveness of the rule base. The slight improvement accuracy is attributed to the current incompleteness of the identification rule base. As the rule base continues to be refined and expanded, its effectiveness in identifying and classifying sensitive attributes is expected to improve accordingly. For example, in System 4, the attribute \"risk_info\" in the table \"major_risk\" was labeled as Level 3 (strong sensitive) by the expert. However, since the data had undergone a desensitization process, the corresponding entries for this attribute in the dataset were empty. As a result, it could not be identified using the identification rules. Based on the attribute name, we infer that it likely falls under the category of semi-canonical data. To verify the effectiveness of the identification rule, we manually constructed sample data for testing. We randomly generated 800 data entries containing the keyword \"risk\" for testing purposes. The calculated sensitivity score was 0.2, corresponding to a sensitivity Level 3 (strong sensitive). Subsequently, classification accuracy can be further improved by allowing experts to add additional identification rules based on the characteristics of the data. The primary role of classification rules is not to improve the accuracy of our method, but rather to adapt the identification and classification process to better meet the practical requirements of sensitive data protection in the railway domain. For example, as shown in Table 10, for the attribute \"id\" in the \"big_view_sxoptimizeintersect\" of System 3, our method assigns it as Level 3. However, according to the guidance provided by experts in the railway domain, \"id\" and field name containing \"_id\" serve as primary keys or foreign keys in data tables. These are not considered sensitive information and should therefore be assigned as Level 1. Accordingly, we adjusted the sensitivity levels of attributes containing \"id\" or \"_id\" to Level 1 by configuring the classification rules. Similarly, fields containing \"date\" are time-related and generally do not include sensitive information; therefore, these attributes can also be reclassified by applying appropriate classification rules. At the same time, the rule base also supports expiry management for sensitive attribute labels, enabling the timely revocation of expired sensitive attributes. As an example, the sensitive data \"risk\" is designated as sensitive until \"31 December 2027\". After this period, its sensitivity status is revoked. This time-based sensitivity adjustment mechanism is designed to meet the practical needs of expiry in railway data protection. Furthermore, a front-end interface is provided to enable domain experts to manage the rule base by adding or modifying entries, thereby supporting ongoing rule maintenance and refinement. Meanwhile, the intermediate processes of sensitive data identification and classification can be monitored in real time through the front-end interface, allowing for effective control and verification of the classification results.