A novel framework for Chinese personal sensitive information detection

With the rapid development of social networks, the harm caused by the leakage of personal sensitive information is becoming increasingly serious. In order to detect and identify personal sensitive information, existing methods build matching rules to detect specific sensitive entities and use machine learning methods to classify sensitive text. These methods face challenges in context analysis and adapting to Chinese language characteristics. This paper proposes CPSID, a method for detecting Chinese personal sensitive information. On the one hand, CPSID utilises rule matching to detect specific personal sensitive information only containing letters and numbers. More importantly, CPSID constructs a sequence labelling model named EBC (ELECTRA-BiLSTM-CRF) to detect more complex personal sensitive information that consist of Chinese characters. The EBC model uses the latest ELECTRA algorithm to implement word embedding, and uses BiLSTM and CRF models to extract personal sensitive information, which can detect Chinese sensitive entities accurately by analysing context information. The model achieves an F1 score of 94.09% on Chinese datasets, outperforming other similar models. Additionally, experiments on real data show CPSID has a better detection result than individual methods (rule matching or sequence labelling).