{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:25:18Z","timestamp":1773156318913,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Special Fund for Central Government-Guided Local Science and Technology Development\u2014High-Level New-Type R&amp;D Institutions","award":["202407a12020002"],"award-info":[{"award-number":["202407a12020002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The graded management of structured sensitive data has become a key challenge in data security governance, particularly amid digital transformation in sectors such as government, finance, and healthcare. The existing methods suffer from limited generalization, low efficiency, and reliance on static rules. This paper proposes PPM-SACG, a privacy preference matrix-based model for sensitive attribute classification and grading. The model adopts a three-stage architecture: (1) composite sensitivity metrics are derived by integrating information entropy and group privacy preferences; (2) domain knowledge-guided clustering and association rule mining improve classification accuracy; and (3) mutual information-based hierarchical clustering enables dynamic grouping and grading, incorporating high-sensitivity isolation. Experiments using real-world vehicle management data (50 attributes, 3000 records) and user privacy surveys verify the method\u2019s effectiveness. Compared with existing approaches, PPM-SACG doubles computational efficiency and supports scenario-aware deployment, offering enhanced compliance and practicality for structured data governance.<\/jats:p>","DOI":"10.3390\/fi17090384","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T14:18:49Z","timestamp":1756217929000},"page":"384","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Multi-Scene Automatic Classification and Grading Method for Structured Sensitive Data Based on Privacy Preferences"],"prefix":"10.3390","volume":"17","author":[{"given":"Yong","family":"Li","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei 230026, China"},{"name":"High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongcheng","family":"Wu","sequence":"additional","affiliation":[{"name":"High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7593-0312","authenticated-orcid":false,"given":"Jinwei","family":"Li","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei 230026, China"},{"name":"High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liyang","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"415","DOI":"10.51594\/csitrj.v5i2.791","article-title":"Business intelligence in the era of big data: A review of analytical tools and competitive advantage","volume":"5","author":"Adewusi","year":"2024","journal-title":"Comput. Sci. IT Res. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Herath, H., Herath, H., Madhusanka, B., and Guruge, L.G.P.K. (2024). Data protection challenges in the processing of sensitive data. Data Protection: The Wake of AI and Machine Learning, Springer Nature.","DOI":"10.1007\/978-3-031-76473-8_8"},{"key":"ref_3","unstructured":"Protection Regulation (2018). General data protection regulation. Intouch, 25, 1\u20135."},{"key":"ref_4","unstructured":"PCI Security Standards Council (2025, August 20). Data Security Standard. Requirements and Security Assessment Version 3. Available online: https:\/\/listings.pcisecuritystandards.org\/documents\/PCI-DSS-v4-0-SAQ-D-Service-Provider.pdf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1007\/s10207-022-00607-5","article-title":"A systematic overview on methods to protect sensitive data provided for various analyses","volume":"21","author":"Templ","year":"2022","journal-title":"Int. J. Inf. Secur."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"e4983","DOI":"10.1002\/ett.4983","article-title":"Sensitive data identification for multi-category and multi-scenario data","volume":"35","author":"Cui","year":"2024","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102444","DOI":"10.1016\/j.datak.2025.102444","article-title":"G2MBCF: Enhanced Named Entity Recognition for sensitive entities identification","volume":"159","author":"Tian","year":"2025","journal-title":"Data Knowl. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e4876","DOI":"10.1002\/ett.4876","article-title":"A privacy-sensitive data identification model in online social networks","volume":"35","author":"Yi","year":"2024","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104108","DOI":"10.1016\/j.autcon.2021.104108","article-title":"Rule-based information extraction for mechanical-electrical-plumbing-specific semantic web","volume":"135","author":"Wu","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e1549","DOI":"10.1002\/wics.1549","article-title":"Challenges and opportunities beyond structured data in analysis of electronic health records","volume":"13","author":"Tayefi","year":"2021","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","unstructured":"Cai, L., Zhou, Y., Ding, Y., Jiang, J., and Yang, S.-H. (2022, January 10). Utilizing lexicon-enhanced approach to sensitive information identification. Proceedings of the 2022 27th International Conference on Automation and Computing (ICAC), Bristol, UK.","DOI":"10.1109\/ICAC55051.2022.9911164"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1177\/0165551518802522","article-title":"Text classification for cognitive domains: A case using lexical, syntactic and semantic features","volume":"45","author":"Qiao","year":"2019","journal-title":"J. Inf. Sci."},{"key":"ref_16","first-page":"2883","article-title":"A novel approach of sensitive data classification using convolution neural network and logistic regression","volume":"8","author":"Gitanjali","year":"2019","journal-title":"Int. J. Innov. Technol. Explor. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Shen, X., and Yang, Y. (2019). The classification of Chinese sensitive information based on BERT-CNN. International Symposium on Intelligence Computation and Applications, Springer.","DOI":"10.1007\/978-981-15-5577-0_20"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4656837","DOI":"10.1155\/2022\/4656837","article-title":"KGDetector: Detecting Chinese Sensitive Information via Knowledge Graph-Enhanced BERT","volume":"2022","author":"Cong","year":"2022","journal-title":"Secur. Commun. Netw."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Timmer, R.C., Liebowitz, D., Nepal, S., and Kanhere, S.S. (2022, January 14). Can pre-trained transformers be used in detecting complex sensitive sentences?\u2014A monsanto case study. Proceedings of the 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), Atlanta, GA, USA.","DOI":"10.1109\/TPSISA52974.2021.00010"},{"key":"ref_20","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_21","first-page":"3077","article-title":"Algorithm for Identification and Classification of Sensitive Attributes in Structured Data Sets","volume":"37","author":"He","year":"2020","journal-title":"Appl. Res. Comput."},{"key":"ref_22","unstructured":"Hao, W. (2023). Research on Address Sensitive Data Identification Method Based on Machine Learning. [Master\u2019s Thesis, Beijing Jiaotong University]."},{"key":"ref_23","unstructured":"Zhang, Y. (2021). Key Technologies and Systems for Sensitive Data Anonymization and Watermarking in Structured Data. [Master\u2019s Thesis, Beijing University of Posts and Telecommunications]."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Guan, X., Zhou, C., and Cao, W. (2022, January 15\u201317). Research on Classification Method of Sensitive Structural Data of Electric Power. Proceedings of the 2022 IEEE 12th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China.","DOI":"10.1109\/ICEIEC54567.2022.9835092"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Y. (2022). Cross-Cultural Privacy Differences. Modern Socio-Technical Perspectives on Privacy, Springer International Publishing.","DOI":"10.1007\/978-3-030-82786-1_12"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/9\/384\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:33:12Z","timestamp":1760034792000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/17\/9\/384"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,26]]},"references-count":25,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["fi17090384"],"URL":"https:\/\/doi.org\/10.3390\/fi17090384","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,26]]}}}