Adaptive Data Masking-based Named Entity Recognition

To address the deficiencies of traditional data masking techniques, this paper focuses on the identification of unstructured sensitive data, and constructs an adaptive data masking-named entity recognition model (Adm-NER). Based on the Bi-LSTM-CRF model, Adm-NER applies adversarial transfer learning to the field of data desensitization and introduces self-attention mechanism, which can effectively identify sensitive data in the lack of sample fields. The results of five comparative experiments show that Adm-NER has significantly improved the accuracy of identifying sensitive data. In addition, the transfer learning experiment proves that Adm-NER can adaptively learn common features by using large-scale labeled samples to achieve accurate positioning and recognition of sensitive data in the lack of sample field, which is conducive to subsequent data desensitization. Adm-NER provides a new idea for the intelligent design of big data masking systems.