{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:41:12Z","timestamp":1775745672204,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100015282","name":"China Academy of Railway Sciences Corporation Limited","doi-asserted-by":"publisher","award":["2023YJ356"],"award-info":[{"award-number":["2023YJ356"]}],"id":[{"id":"10.13039\/501100015282","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/fi17070294","type":"journal-article","created":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T11:37:51Z","timestamp":1751283471000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ICRSSD: Identification and Classification for Railway Structured Sensitive Data"],"prefix":"10.3390","volume":"17","author":[{"given":"Yage","family":"Jin","sequence":"first","affiliation":[{"name":"School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongming","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science & Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanhua","family":"Wu","sequence":"additional","affiliation":[{"name":"The Center of National Railway Intelligent Transportation System Engineering and Technology, Beijing 100081, China"},{"name":"Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingxin","family":"Li","sequence":"additional","affiliation":[{"name":"The Center of National Railway Intelligent Transportation System Engineering and Technology, Beijing 100081, China"},{"name":"Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,30]]},"reference":[{"key":"ref_1","first-page":"33","article-title":"Risk analysis and countermeasures of railway network data security","volume":"31","author":"Wei","year":"2022","journal-title":"Railw. Comput. Appl."},{"key":"ref_2","unstructured":"Wang, Y. (2019). Research and Implementation of Structured Privacy Data Desensitization System. [Master\u2019s Thesis, Harbin Institute of Technology]."},{"key":"ref_3","first-page":"1465","article-title":"Privacy and data utility metric model for structured data","volume":"37","author":"Xie","year":"2020","journal-title":"Appl. Res. Comput. Yingyong Yanjiu"},{"key":"ref_4","unstructured":"He, W. (2020). Research on Intelligent Recognition Algorithm and Adaptive Protection Model of Sensitive Data. [Master\u2019s Thesis, Guizhou University]."},{"key":"ref_5","unstructured":"Li, Q. (2023). Railway Data Security Governance System and Privacy Computing Technology Research. [Master\u2019s Thesis, China Academy of Railway Sciences]."},{"key":"ref_6","unstructured":"Chen, J. (2020). Research and Application of Sensitive Attribute Recognition Method for data PUBLISHING. [Master\u2019s Thesis, Chengdu University of Technology]."},{"key":"ref_7","first-page":"4656837","article-title":"KGDetector: Detecting Chinese Sensitive Information via Knowledge Graph-Enhanced BERT","volume":"2022","author":"Cong","year":"2022","journal-title":"Secur. Commun. Networks"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"119924","DOI":"10.1016\/j.eswa.2023.119924","article-title":"CASSED: Context-based Approach for Structured Sensitive Data Detection","volume":"223","author":"Petric","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, M., Liu, J., and Yang, Y. (2024). Automated Identification of Sensitive Financial Data Based on the Topic Analysis. Future Internet, 16.","DOI":"10.3390\/fi16020055"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zu, L., Qi, W., Li, H., Men, X., Lu, Z., Ye, J., and Zhang, L. (2024). UP-SDCG: A Method of Sensitive Data Classification for Collaborative Edge Computing in Financial Cloud Environment. Future Internet, 16.","DOI":"10.3390\/fi16030102"},{"key":"ref_11","first-page":"7","article-title":"Intelligent identification and classification and grading method of railway sensitive data based on hierarchical topic analysis","volume":"33","author":"Jiang","year":"2024","journal-title":"Railw. Comput. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e1597","DOI":"10.1002\/wics.1597","article-title":"Cluster analysis: A modern statistical review","volume":"15","author":"Jaeger","year":"2023","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_14","first-page":"4","article-title":"Data mining: Concepts and techniques","volume":"10","author":"Mining","year":"2006","journal-title":"Morgan Kaufinann"},{"key":"ref_15","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume":"Volume 5","author":"MacQueen","year":"1967","journal-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Song, H., Lee, J.G., and Han, W.S. (2017, January 13\u201317). PAMAE: Parallel k-medoids clustering with high accuracy and efficiency. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada.","DOI":"10.1145\/3097983.3098098"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kaufman, L., and Rousseeuw, P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons.","DOI":"10.1002\/9780470316801"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/235968.233324","article-title":"BIRCH: An efficient data clustering method for very large databases","volume":"25","author":"Zhang","year":"1996","journal-title":"ACM Sigmod Rec."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1007\/BF02948845","article-title":"A fast algorithm for mining association rules","volume":"15","author":"Huang","year":"2000","journal-title":"J. Comput. Sci. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Borgelt, C. (2005, January 21). An Implementation of the FP-growth Algorithm. Proceedings of the 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, Chicago, IL, USA.","DOI":"10.1145\/1133905.1133907"},{"key":"ref_21","unstructured":"Han, J., Pei, J., and Kanber, M. (2012). Data Mining: Concepts and Techniques, China Machine Press."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ku\u017eina, V., Vu\u0161ak, E., and Jovi\u0107, A. (October, January 27). Methods for automatic sensitive data detection in large datasets: A review. Proceedings of the 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO52101.2021.9596735"},{"key":"ref_23","first-page":"933","article-title":"Research on Data Classification and Grading Methods Based on the Data Security Law","volume":"7","author":"Gao","year":"2021","journal-title":"Inf. Secur. Res."},{"key":"ref_24","unstructured":"White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., and Schmidt, D.C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv."},{"key":"ref_25","unstructured":"DAIR.AI (2025, June 27). Prompt Engineering Guide. [EB\/OL]. Available online: https:\/\/www.promptingguide.ai\/introduction\/elements."},{"key":"ref_26","unstructured":"Nigh, M. (2025, June 27). ChatGPT3-Free-Prompt-List: A Free Guide for Learning to Create ChatGPT3 Prompts. [EB\/OL]. Available online: https:\/\/github.com\/mattnigh\/ChatGPT-Free-Prompt-List\/blob\/main\/dist\/markdown\/prompting-guide.md."},{"key":"ref_27","first-page":"1","article-title":"Generalizing from a few examples: A survey on few-shot learning","volume":"53","author":"Wang","year":"2020","journal-title":"ACM Comput. Surv. (csur)"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"56","DOI":"10.54254\/2755-2721\/2025.23147","article-title":"Data service platform oriented to enhance capabilities of digital railway infrastructure","volume":"34","author":"Liu","year":"2025","journal-title":"Railw. Comput. Appl."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/7\/294\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:01:44Z","timestamp":1760032904000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/7\/294"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":28,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["fi17070294"],"URL":"https:\/\/doi.org\/10.3390\/fi17070294","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,30]]}}}