中图分类号：TP18；TM615 文献标志码：A DOI: 10.16157/j.issn.0258-7998.267992
中文引用格式： 李鑫，王吉华，王涛，等. 基于大语言模型的非结构化文本敏感信息识别方法[J]. 电子技术应用，2026，52(10)：60-66.
英文引用格式： Li Xin，Wang Jihua，Wang Tao，et al. Identification method for sensitive information in unstructured text based on large language models[J]. Application of Electronic Technique，2026，52(10)：60-66.

Identification method for sensitive information in unstructured text based on large language models

Abstract： With the rapid development of e-commerce and social networking platforms, core business of the enterprise and other sensitive information has increased significantly, raising growing concerns about privacy leakage. To address the need for automatic identification and secure processing of sensitive information in unstructured text, this study investigates the application of large language models (LLMs) for detecting and sanitizing enterprise information. Using user texts from platforms such as Amazon, patient.info, and Facebook, a framework based on LLMs for sensitive information identification and desensitization is constructed. Through prompt engineering, the model identifies and evaluates the sensitivity of disclosed information and rewrites the original content under semantic-preservation constraints, thereby minimizing sensitive information disclosure while maintaining semantic consistency. ChatGPT is employed as the experimental model, and empirical results show that the proposed method can effectively identify sensitive health information in user-generated texts and generate sanitized versions with high semantic fidelity, providing a feasible approach for privacy protection and information security governance in unstructured text.

Key words : large language models；unstructured text；sensitive information detection；text sanitization