NLP-Driven Privacy Solutions for Medical Records Using Transformer Architecture

This study aims to explore innovative applications of natural language processing (NLP) in the privacy protection of healthcare records in the United States. Faced with the increasing amount of industry data and the risk of privacy disclosure, traditional security measures have made it difficult to meet the needs of modern healthcare. Therefore, this paper proposes a privacy protection system based on NLP, which realizes efficient encryption and sensitive information identification of medical text data through a deep learning model, especially Transformer architecture. The system uses advanced word embedding technology, such as Word2Vec, to capture the nuances of medical terms and perform precise named entity identification through a conditional random field (CRF) model, thus ensuring accurate retrieval of medical information while protecting patient privacy. In addition, the system introduces a context-aware ambiguity resolution mechanism to deal with the complex context-dependent problem in medical records. Through the empirical study of American medical and health records, this system shows its excellent performance in reducing data access delay, improving access control stability, and minimizing encryption costs, with excellent security index performance, providing strong technical support for big data privacy protection in the medical industry.