Legal Privacy Protection Machine Learning Method Based on Word2Vec Algorithm
Rongrong Wang · International Journal of Information Security and Privacy · 2024
This study uses Word2Vec's word vector representation technology to finely capture the semantic relationships of vocabulary in legal texts through the Skip-gram model. By introducing Hierarchical Softmax optimization, a legal privacy protection model based on Word2Vec algorithm is ultimately designed. The results showed that the model performed better than other comparative algorithms in both the macro classification performance (Fl_macro) and the micro classification performance (Fl_micro). In practical legal sensitive word recognition tasks, the accuracy, recall rate, and F1 score of the model reached 92.56%, 88.78%, and 90.62%, respectively. Therefore, the proposed model effectively improved the accuracy of identifying sensitive legal privacy words and providing new methods for the personal information security protection system.