CGDSClass: a sensitive personal information detection and classification solution for Chinese open government data

Purpose Chinese government documents require sensitivity reviews to detect and protect sensitive personal information before public release. However, this process is often time-consuming and labor-intensive due to the lack of robust automated detection and classification systems, especially considering the complexities of Chinese morphology, grammar and semantics, which challenge existing solutions. Design/methodology/approach This study proposes a parallel dual-channel hybrid neural network model for the detection and classification of sensitive information. The model leverages Bidirectional Encoder Representations from Transformers (BERT) for text vector representation, combined with Text Convolutional Neural Networks (TextCNN) and Bi-directional Long Short-Term Memory (BiLSTM) networks to extract both local and global semantic features. The integration of an attention mechanism further enhances the model’s capacity by emphasizing critical terms, thereby improving the precision of sensitive information classification. Findings The effectiveness of the proposed model, CGDSClass, was validated through comparative analysis with nine existing models across two datasets. The results demonstrate that CGDSClass significantly outperforms these models, indicating a superior capability in handling the specific challenges of sensitive information detection in Chinese government documents. Originality/value As the first study to address sensitive information detection and classification in the context of Chinese government open data, this research offers a novel theoretical and technical foundation. CGDSClass not only improves the accuracy of classification but also enhances early warning and monitoring capabilities, thereby reducing the risk of data breaches and advancing the openness of government data practices.