## 2 Related Work
### Structured Sensitive Data Classification  
[PERSON] et al. [8] conducted a comprehensive investigation into the classification approach for structured sensitive data in the realm of electric power. They introduced a hierarchical identification technique, which initially identifies attributes within the database as sensitive data and subsequently categorizes them based on the specific characteristics of the sensitive information. Furthermore, different levels of sensitivity are assigned to these attributes in accordance with the varying permissions of the involved users. On the other hand, [PERSON] et al. [9] focused on safeguarding data stored in the cloud by extracting sensitive data components and post-encryption using Attribute-Based Encryption (ABE), and proposed a classification technique based on fuzzy rule analysis to effectively categorize attributes within structured data. In the healthcare domain, [PERSON] et al. [10] combined domain experts and expert systems to assign sensitivity scores to attributes, enabling the identification of sensitive data through techniques such as random sampling and multiple scanning, eliminating the need for data cleansing before identification.  
However, the standalone accuracy of rule-based sensitive data classification methods presents limitations due to the dynamic nature of industry attributes and linguistic context, leading to instances where the algorithm may miss identifying the sensitive data. [PERSON] et al. [11] devised a strategy that involves semantically designating which concepts constitute sensitive information, thereby ascertaining sensitive content within structured data. Subsequently, the attributes in the database that semantically correspond to these concepts are retrieved. While this algorithm exhibits robust performance on smaller datasets, its scalability to larger datasets is a challenge. In response to the limitations of individual sensitive attribute detection, [PERSON]. [12] introduced correlation rules to identify interconnected sensitive attributes. Similarly, [PERSON]. [13] proposed determining the correlation among sensitive attributes in structured data through a multidimensional bucket grouping technique, which enables the establishment of sensitive categories and levels based on attribute correlations.  
[PERSON]. [14] employed machine learning techniques, including Bert models and regular expressions, for real-time active identification, classification, and validation of sensitive data. Similarly, [PERSON]. [15] harnessed NLP tools (NLTK, Stanford, and CoreNLP) to identify and validate personally identifiable information within datasets. Recent advancements have embraced deep learning-based NER models, showcasing their potential in automatic feature discovery for enhanced classification or detection [16]. Furthermore, [PERSON] et al [17] introduced NER techniques for structured data, constructing the Text Generation Module (TG Module) and Named Entity Recognition Module (NER Module) to generate sentences and recognize entities, respectively. While the application of AI models has indeed enhanced the accuracy of sensitive data recognition to a certain extent, the models often lack domain-specific knowledge at their inception. For instance, they may overlook the recognition of synonyms, leading to suboptimal performance in real-world engineering applications.