# Research on Intelligent Perception Algorithm for Sensitive Information
## 5 Conclusions  
In this research, we propose an intelligent perception algorithm (TSIIP) and the detection and recognition model KG-ERNIE for the problem of text sensitive information perception. We design an algorithm to identify sensitive words at the word level and sensitive statements at the statement level in order to obtain the evaluation score of the final text for the issue of considering only single-level semantics. We encode the input text using an entity embedding model based on the ERNIE pre-training model and knowledge graph and then use our intelligent perception algorithm to intelligently perceive sensitive information on the input text using a convolutional neural network (CNN) as the underlying architecture. For the dataset, we created a sensitive vocabulary based on THUNews and Chinese Wikipedia, as well as the Chinese sensitive information dataset JWBD. Numerous experiments were performed on this dataset, and the F1 and F2 scores for the KG-ERNIE model and the TSIIP method proposed in this study were 0.938 (0.6% improvement) and 0.946 (1% improvement), respectively. Thus, it can be said that the TSIIP algorithm and the KG-ERNIE model surpass other current methods. Specific application scenarios may include: applying the method to the review of documents transmitted by enterprises in the network to prevent the leakage of sensitive information of enterprises or individuals by perceiving whether the transmitted text contains sensitive information; it can also be applied to the review of detecting whether sensitive information is contained in electronic documents of countries or governments to improve the efficiency and quality of the review of electronic text documents.  
Although this solution has achieved good results, there is still room for improvement. In future work, we can continue to expand and improve the sensitive vocabulary and sensitive sentences library and further investigate this part of the classifier to improve both the recognition precision and the recall rate of sensitive information. Furthermore, we intend to further develop our current binary classification system into a quantitative assessment of the text sensitivity level, with sensitive information text being classified  
Figure 6: Variation of precision and recall.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** & **F2 Score** \\ \hline KG-ERNIE+TSIIP & **0.851** & **0.867** & **0.859** & **0.864** \\ KGDetector & 0.701 & 0.681 & 0.691 & 0.685 \\ CNN-BiLSTM & 0.684 & 0.703 & 0.693 & 0.699 \\ TextCNN & 0.653 & 0.681 & 0.667 & 0.675 \\ BERT-BiLSTM-Attention & 0.710 & 0.702 & 0.706 & 0.704 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results from the company’s private data. The bold numbers are the best results.  
into three levels: Top Secret Level, Confidential Level, and Secret Level. We will further investigate this part of the classifier to improve both the recognition accuracy and the recall rate of sensitive information.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON]; project administration, [PERSON] and [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant 61962005) for financial supports.  
Not applicable.  
We thank all of the authors of the primary studies included in this article.  
The authors declare no conflict of interest.  
Samples of the compounds are available from the authors.  
The following abbreviations are used in this manuscript:  
Bert Bidirectional Transformers ERNIE KRKnowledge Graph