# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
###### Abstract
Furthermore, a number of recent studies have explored hybrid approaches that integrate machine learning with topic modeling and related techniques to improve the identification and grading of sensitive data. [PERSON] [7] et al. proposed a method that combines a knowledge graph and BERT for document-level sensitive information classification. Specifically, they trained a Chinese entity encoding model based on a knowledge graph to capture domain-specific semantics. In parallel, they utilized BERT to represent sensitive information at the contextual level, enabling more accurate classification. [PERSON] [8] et al. developed a machine learning-driven framework for sensitive data identification and classification. The method leverages BERT for multi-label classification, using the column data as context. In addition, it combines regular expressions and keyword lists, with the match rate (i.e., the percentage of data matched by patterns) used to estimate the probability of an attribute belonging to a specific sensitivity category. [PERSON] [9] et al. proposed a sensitive data classification method tailored for the financial domain. Their approach combines thematic analysis and cluster analysis to construct thematic word sets, with sensitivity levels assigned based on domain expert knowledge. [PERSON] [10] et al. proposed an automated sensitive data categorization approach. They classify entities into three categories--no rules, weak rules, and strong rules--depending on whether or not they exhibited identifiable fixed patterns. To achieve sensitive data recognition, the method integrates regular expressions and keyword lists, with the latter enhanced through a synonym recognition model. [PERSON] [11] et al. proposed a hierarchical topic analysis-based approach for identifying and classifying sensitive data. The sensitivity level of each attribute was computed through a weighted summation of segmented attribute names and topic terms. To further improve classification accuracy, the authors applied a cohesive hierarchical clustering algorithm to adjust the hierarchical structure of attributes. Several of the aforementioned studies perform sensitivity classification at the individual attribute level without considering the inter-attribute correlations. This limitation may result in the omission of certain sensitive attributes that can only be identified through their relationships with other attributes. In addition, most of the aforementioned literature focuses on generic or domain-specific identification and classification methods, such as those in the financial domain. In contrast, our approach is specifically tailored to the characteristics and requirements of the railway domain. In addition, existing studies have not adequately considered the characteristics of different data types within structured data during the sensitive data identification stage. In particular, traditional identification methods are less effective when dealing with data based on Chinese characters. Additionally, during the identification of sensitive data, traditional data mining methods typically focus on an attribute's association relationship within a single scenario and fail to effectively capture association relationships across the entire system. This limitation results in shallow and incomplete association relationship mining, which may cause certain sensitive attributes to be overlooked and ultimately lead to suboptimal performance in identification and classification. Furthermore, in the final classification stage, existing approaches generally adopted threshold-based static classification methods, which suffer from several limitations, including subjectivity in threshold selection and a lack of flexibility in adapting to varying data characteristics. To address the above issues, we classify structured data into canonical data and semi-canonical data based on different data patterns, and perform identification for each type separately to improve identification accuracy.