# ICRSSD: Identification and Classification for Railway Structured Sensitive Data
###### Abstract  
The rapid growth of the railway industry has resulted in the accumulation of large structured data that makes data security a critical component of reliable railway system operations. However, existing methods for identifying and classifying often suffer from limitations such as overly coarse identification granularity and insufficient flexibility in classification. To address these issues, we propose ICRSSD, a two-stage method for identification and classification in terms of the railway domain. The identification stage focuses on obtaining the sensitivity of all attributes. We first divide structured data into canonical data and semi-canonical data at a finer granularity to improve the identification accuracy. For canonical data, we use information entropy to calculate the initial sensitivity. Subsequently, we update the attribute sensitivities through cluster analysis and association rule mining. For semi-canonical data, we calculate attribute sensitivity by using a combination of regular expressions and keyword lists. In the classification stage, to further enhance accuracy, we adopt a dynamic and multi-granularity classified strategy. It considers the relative sensitivity of attributes across different scenarios and classifies them into three levels based on the sensitivity values obtained during the identification stage. Additionally, we design a rule base specifically for the identification and classification of sensitive data in the railway domain. This rule base enables effective data identification and classification, while also supporting the expiry management of sensitive attribute labels. To improve the efficiency of regular expression generation, we developed an auxiliary tool with the help of large language models and a well-designed prompt framework. We conducted experiments on a real-world dataset from the railway domain. The results demonstrate that ICRSSD significantly improves the accuracy and adaptability of sensitive data identification and classification in the railway domain. 29 May 202525 June 202528 June 20  
force for development in the railway industry, and its security has become an indispensable part of the stable operation of railway systems. At present, the railway industry has moved away from traditional paper-based storage and adopted digital formats, which increases the risk of data theft by malicious actors [1]. Among a large number of data in the railway domain, there is a significant amount of sensitive data related to national security and citizens' privacy. How to successfully identify and effectively protect them has always been a difficult problem to solve. After investigating a large number of railway data, we found that most of it is structured data. We thus need to design a set of methods for sensitive data identification and automatic classification for structured data in order to help the railway domain identify and protect sensitive data. A number of sensitive information identification methods have been proposed for structured data. [PERSON] [2] classified structured data into three forms: single-attribute columns, multi-attribute columns, and the entire dataset. They proposed a double Bloom filter-based identification method for single-attribute columns and a non-frequent attribute mining algorithm for multi-attribute columns. [PERSON] [3] et al. proposed a privacy and data utility metric model for structured data. Their approach transforms structured data into matrices using numerical methods and introduces a privacy preference function to describe and analyze the impact of privacy-preserving mechanisms on sensitive data matrices. The works in [4; 5; 6] proposed a method for identifying and classifying structured sensitive data by leveraging information entropy to assess attribute sensitivity, and employing machine learning techniques such as clustering and association rule mining to classify sensitive attributes.