## 2 Related Works  
### API Sensitive Data Identification  
The primary aim of API attacks is to steal and tamper with sensitive data, making the integration of API protection measures with sensitive data monitoring a practical and effective approach. API sensitive data identification serves as a crucial prerequisite for safeguarding data privacy and security. Intelligent algorithms automate the process of identifying and classifying sensitive information from vast amounts of data, thereby reducing human errors and enhancing efficiency. Currently, two primary categories of algorithms and technologies are employed for sensitive data identification: rule-based and machine learning-based. 1. Rule-Based Algorithms: Rule-based methods utilize predefined patterns and rules to match sensitive data. For instance, regular expressions can be employed to detect common sensitive information such as social security numbers, credit card numbers, email addresses, etc. These patterns can be manually defined or automatically generated. The advantages lie in their simplicity, directness, and ease of implementation, as well as their strong interpretability with clear and concise rules. However, they suffer from difficulties in maintenance and updating, and lack flexibility in dealing with unknown patterns and highly variable data. 2. Machine Learning Algorithms: Machine learning algorithms identify sensitive data by training models. These models can automatically recognize and classify sensitive information within data. In supervised learning, classifiers are trained using labeled training data, with common algorithms including Support Vector Machines (SVMs), Random Forest, and K-Nearest Neighbor (KNN), primarily used for identifying sensitive information in structured data. In unsupervised learning, sensitive data are identified by discovering patterns and anomalies within the data, with algorithms such as clustering (e.g., K-Means) and Isolation Forest commonly employed for detecting anomalies and sensitive information in unlabeled data. 3. Hybrid Methods: Hybrid methods combine the strengths of multiple technologies and algorithms to improve the accuracy and efficiency of sensitive data identification. For example, a hybrid approach might integrate rule-based methods with machine learning methods, initially screening data with rules and then refining the classification with machine learning models. 4. Privacy-Enhancing Technologies: Privacy-enhancing technologies enable sensitive data identification while ensuring data privacy. For instance, federated learning allows devices to collaboratively train models without sharing raw data, thereby protecting data privacy. This paper focuses on implementing API sensitive data identification through the fusion of federated learning and large language models. Sensitive data identification is often combined with privacy-preserving computing technologies such as federated learning, differential privacy, and homomorphic encryption, to enable data analysis and processing while protecting user privacy. In the field of smart healthcare, applications of sensitive data identification based on federated learning have addressed privacy protection issues in electronic health record management, medical image analysis, and other areas, such as predicting heart disease hospitalizations based on patients' electronic health records [9]. A collaborative model based on federated learning and blockchain has been constructed using data from multiple institutions to identify COVID-19 patients from lung CT images [10]. In the field of smart transportation, [11] proposes a traffic resource scheduling method based on federated learning, which significantly improves group fairness and travel efficiency. Most of the aforementioned research efforts focus on designing context understanding capabilities from the perspectives of machine learning and deep learning, resulting in poor generalizability and high false positive rates, and thus failing to address complex and diverse application scenarios.