A Transformer-CNN-SVM Based Architecture for Legal Risk Assessment in Data Privacy Infringements

Under the wave of digitalization, privacy rights and data protection face severe challenges. This study focuses on the application of artificial intelligence in this legal issue and constructs an intelligent legal privacy protection system (ILPPS). By integrating real data from multiple fields such as finance, e-commerce, social networking, medical care, and government affairs and corresponding legal provisions, an experimental system is built. Using accuracy, recall, and F1 value as evaluation indicators, ILPPS is compared with models such as C4.5, Naive Bayes, BERT-SVM, CNN-LSTM, and GRU-SVM. The experimental results show that ILPPS performs well on data sets in various fields. For example, in the financial field, the accuracy rate is 0.87, the recall rate is 0.84, and the F1 value is 0.85; comprehensive analysis of various fields shows that ILPPS has an average accuracy rate of 0.86, a recall rate of 0.83, and an F1 value of 0.84. This shows that ILPPS is significantly superior to traditional models in data privacy risk assessment and infringement judgment, providing enterprises and legal institutions with more effective data privacy protection tools, enriching the research in the intersection of computer technology and law, and promoting the healthy development of social digitalization.