# Research on Intelligent Perception Algorithm for Sensitive Information
## 2 Related Work  
With the proliferation of information on a global scale, intelligent perception of sensitive information has become an increasingly important area of research. Sensitive information keyword-matching algorithms were utilized in 2015 by [PERSON] et al. [14] to locate classified documents. Since keyword lexicons are manually generated, subjectivity can affect how accurately things are classified. Recurrent neural networks (RNN) were then employed by [PERSON] et al. [15] in 2017 to analyze the syntactic and grammatical structure of texts, find sensitive information in text documents, and assess the sensitivity values of the semantic component of the text structure. [PERSON] et al. [16] then developed a sensitive phrase recurrent neural network with an Acc value of 76.78 % to capture the intricacy of detecting sensitive information. In 2018, [PERSON] et al. [17] presented their ideas and introduced a new topic-tracking algorithm. It monitors sensitive words over a period of time and assigns different weights to them, taking into account the number of occurrences of sensitive words in the text and the different positions in the text where the sensitive words appear, but the algorithm ignores the semantic information before and after the words as well as the semantic information at the utterance level. Then, [PERSON] et al. [18] presented a novel approach for textual CNN-based sensitive information identification. More quickly and accurately than previous models and techniques, the training time of the detection model was reduced while maintaining detection accuracy. A framework to more comprehensively extract data features in 2020 was put forth by [PERSON] et al. [19] in order to improve detection outcomes. The Bi-LSTM and CNN structures are the foundation of the system. Convolutional neural networks are utilized to effectively extract local features from unstructured documents, and Bi-LSTM networks are constructed to extract global features. In the same year, [PERSON] et al. designed a brand-new framework to identify sensitive data using a network traffic recovery method [20]. A scalable convolutional neural network (CNN) and a bidirectional long short-term memory model (Bi-LSTM) with a multi-channel structure were proposed in 2021 by [PERSON] et al. [21] to detect sensitive information in Chinese texts, analyze the sentiment tendency of Chinese texts, and introduce attention mechanisms to improve the model's performance. Due to the widespread use of the Transformer model [10] and the Bert model [11] in 2019, a number of academics have presented numerous solutions based on these two models. A sensitive information classification model based on BERT-CNN was presented by [PERSON] et al. [22], which enhances the word embedding's generalization capability and effectively classifies network sensitive information in short text datasets. Using a variation recognition and similarity calculation method that includes variations in synonyms, pronouns, acronyms, and word forms, followed by a combination of rules, [PERSON] et al. [23]proposed sensitive word detection method uses variation recognition and association analysis to identify and judge sensitive words. However, the running cost is too high, and it is more difficult to apply to constantly changing sensitive words. [PERSON] et al. [24] used a sequence tagging algorithm based on the BERT model to find and delete sensitive data from Spanish clinical literature in 2020 to protect the privacy of personal data. A method to extract sensitive information from unstructured data using a hybrid content-based and context-based extract mechanism was proposed by [PERSON] et al. [25] in 2021. In 2022, [PERSON] et al. [26] combined the BERT framework and knowledge graph to form the KGDetector framework for detecting Chinese sensitive information, and the CNN+FC classifier proposed in the paper was used in their construction of a Chinese sensitive information dataset that was experimented on, and the F1 value was 93.7%. In the same year, [PERSON] et al.