Exploration of Efficient Recognition Techniques for Sensitive Data Based on Feature Fusion

At this stage, data has become essential and important information in people's daily life, and how to automate the identification and classification of sensitive attributes of structured datasets in the production environment, etc., has also become a key issue in data privacy protection. This paper proposes an efficient recognition technique for sensitive data based on feature fusion. The method can effectively improve the accuracy of sensitive data identification, realize the identification and classification of sensitive attributes, and take into account the correlation and association between attributes.

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Publication Details

Journal
Informatica
Published
2024-12-06
DOI
https://doi.org/10.31449/inf.v48i22.6758
Primary Topic
Advanced Measurement and Detection Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploration of Efficient Recognition Techniques for Sensitive Data Based on Feature Fusion

Yixin Jiang, Jian Hu, Guangqian Lu, Lina Zhang et al.
Informatica
Advanced Measurement and Detection Methods
article

Exploration of Efficient Recognition Techniques for Sensitive Data Based on Feature Fusion

Yixin Jiang, Jian Hu, Guangqian Lu, Lina Zhang, Wenqian Xu
article en

Abstract

At this stage, data has become essential and important information in people's daily life, and how to automate the identification and classification of sensitive attributes of structured datasets in the production environment, etc., has also become a key issue in data privacy protection. This paper proposes an efficient recognition technique for sensitive data based on feature fusion. The method can effectively improve the accuracy of sensitive data identification, realize the identification and classification of sensitive attributes, and take into account the correlation and association between attributes.

InformaticaVol. 48(22)
China Southern Power Grid
Openalex Percentile: Top 76%
Advanced Measurement and Detection Methods
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