A Systematic Study on the Privacy Protection Mechanism of Natural Language Processing in Medical Health Records

This study focuses on the privacy protection of American health records in the context of big data and proposes an innovative privacy protection mechanism based on natural language processing (NLP). In the face of an ever-growing environment of medical data, traditional privacy protections are no longer sufficient to address complex security threats. From the technical perspective of NLP, this paper uses advanced technologies such as word embedding and the transformer model to build a system that can understand, analyze, and protect the privacy of medical health records. Through deep learning and self-attention mechanism, the system realizes automatic identification and encryption of sensitive information of medical text data, effectively balancing the needs of data privacy protection and medical information research. The experimental results show that the system significantly improves the security and availability of medical data while protecting personal privacy, and provides a new solution for privacy protection in the medical industry.