Legal Privacy Protection Machine Learning Method Based on Word2Vec Algorithm

Rongrong Wang
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Rongrong Wang: Zhe Jiang J.R.C. Law Firm, China

International Journal of Information Security and Privacy (IJISP), 2025, vol. 19, issue 1, 1-19

Abstract: 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.

Date: 2025
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International Journal of Information Security and Privacy (IJISP) is currently edited by Yassine Maleh

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