IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v17y2025i9p384-d1733102.html
   My bibliography  Save this article

A Multi-Scene Automatic Classification and Grading Method for Structured Sensitive Data Based on Privacy Preferences

Author

Listed:
  • Yong Li

    (University of Science and Technology of China, Hefei 230026, China
    High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Zhongcheng Wu

    (High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Jinwei Li

    (University of Science and Technology of China, Hefei 230026, China
    High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China)

  • Liyang Xie

    (School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China)

Abstract

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’s effectiveness. Compared with existing approaches, PPM-SACG doubles computational efficiency and supports scenario-aware deployment, offering enhanced compliance and practicality for structured data governance.

Suggested Citation

  • Yong Li & Zhongcheng Wu & Jinwei Li & Liyang Xie, 2025. "A Multi-Scene Automatic Classification and Grading Method for Structured Sensitive Data Based on Privacy Preferences," Future Internet, MDPI, vol. 17(9), pages 1-30, August.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:9:p:384-:d:1733102
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/17/9/384/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/17/9/384/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lijun Zu & Wenyu Qi & Hongyi Li & Xiaohua Men & Zhihui Lu & Jiawei Ye & Liang Zhang, 2024. "UP-SDCG: A Method of Sensitive Data Classification for Collaborative Edge Computing in Financial Cloud Environment," Future Internet, MDPI, vol. 16(3), pages 1-24, March.
    2. Meng Li & Jiqiang Liu & Yeping Yang, 2024. "Automated Identification of Sensitive Financial Data Based on the Topic Analysis," Future Internet, MDPI, vol. 16(2), pages 1-17, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yage Jin & Hongming Chen & Rui Ma & Yanhua Wu & Qingxin Li, 2025. "ICRSSD: Identification and Classification for Railway Structured Sensitive Data," Future Internet, MDPI, vol. 17(7), pages 1-24, June.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:17:y:2025:i:9:p:384-:d:1733102. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.