## 4 Results and Discussion
### Results on Language Process Tasks  
In this study, experiments were designed on LLMs to evaluate the performance of the proposed user-centered data privacy protection framework in natural language processing tasks. By comparing the performance of different privacy protection methods across various LLMs, it is possible to verify whether our method can maintain or even enhance the precision, recall, and accuracy of these models while protecting user privacy. This is of practical significance for applications in sensitive data processing fields, such as financial text analysis and social media sentiment analysis. The experimental results show that the metrics of precision, recall, and accuracy for different models are significantly better when applying our method compared to other privacy protection methods. Specifically, in terms of precision, as shown in Table 4, the Transformer model achieves a precision of 0.81 with our method compared to 0.75 with FHE, 0.73 with FL, 0.72 with DP, and 0.70 with DP + FL. The BERT model achieves a precision of 0.84, which is also significantly higher than other methods. For the CLIP, BLIP, and BLIP2 models, our method achieves precisions of 0.86, 0.88, and 0.90, respectively, all significantly outperforming the other methods. These results indicate that our method performs well across different LLMs. In terms of recall, as shown in Table 5, the Transformer model achieves a recall of 0.76 with our method compared to 0.70 with FHE, 0.68 with FL, 0.67 with DP, and 0.64 with DP + FL. The BERT, CLIP, BLIP, and BLIP2 models achieve recalls of 0.78, 0.81, 0.84, and 0.87, respectively, which are all significantly higher than other privacy protection methods. This demonstrates that our framework not only excels in precision but also in recall, effectively capturing and identifying more relevant information. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Our Method** & **FHE** & **FL** & **DP** & **DP + FL** \\ \hline Transformer & 0.81 & 0.75 & 0.73 & 0.72 & 0.70 \\ BERT & 0.84 & 0.78 & 0.76 & 0.74 & 0.72 \\ CLIP & 0.86 & 0.80 & 0.78 & 0.76 & 0.74 \\ BLIP & 0.88 & 0.81 & 0.79 & 0.77 & 0.75 \\ BLIP2 & 0.90 & 0.82 & 0.80 & 0.79 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results on LLMs of precision for different methods. In terms of accuracy, as shown in Table 6, the Transformer model achieves an accuracy of 0.78 with our method, which is higher than 0.72 with FHE, 0.70 with FL, 0.69 with DP, and 0.66 with DP + FL. The BERT, CLIP, BLIP, and BLIP2 models achieve accuracies of 0.80, 0.83, 0.85, and 0.88, respectively, which are also significantly higher than the other methods. This further proves that our method has a significant advantage in improving the overall model performance. From a theoretical analysis perspective, these results can be explained by the mathematical characteristics of the models. The Transformer model uses a self-attention mechanism that performs well in processing long texts. Our method introduces user privacy weights, dynamically adjusting attention weights, allowing it to efficiently process and analyze text data while protecting privacy. The BERT model captures contextual information through a bidirectional Transformer architecture. Our method applies differential privacy techniques in BERT by adding noise during parameter updates, effectively preventing user data leakage while maintaining high precision and high recall. The CLIP model combines multimodal information from language and images. With our method, it can more precisely protect user privacy while performing excellently in processing high-dimensional data. The BLIP and BLIP2 models further optimize multimodal pre-training tasks and datasets. Through our method, these models significantly improve precision, recall, and accuracy in multimodal tasks while protecting user privacy. Mathematically, the performance improvement observed in different models when applying our method can be attributed to several factors.