## 4 Results and Discussion
### Ablation Study on Loss Function  
The purpose of this experimental design was to compare the effects of different loss functions on model performance, specifically evaluating uni-loss, cross-entropy loss, and mean squared error (MSE) across various baseline models. These comparative experiments aim to verify the comprehensive optimization effect of uni-loss in multi-task processing and to explore its application in computer vision and natural language processing tasks. The precision results indicate that uni-loss consistently achieves the best performance across all models. For instance, in computer vision models, FasterRCNN achieves a precision of 0.82 with uni-loss compared to 0.78 and 0.73 with cross-entropy loss and MSE, respectively. The precision for SSD, YOLO, and EfficientDet models under uni-loss is also significantly higher than with the other two loss functions, reaching 0.84, 0.87, and 0.90, respectively. Similarly, in natural language processing models, the precision for Transformer, BERT, CLIP, BLIP, and BLIP2 under uni-loss is 0.81, 0.84, 0.86, 0.88, and 0.90, respectively, which are all higher than with cross-entropy loss and MSE. These results suggest that uni-loss can more effectively optimize model performance and privacy protection, improving precision (Table 11). The recall results show a similar pattern of superiority for uni-loss. FasterRCNN achieves a recall of 0.79 with uni-loss compared to 0.76 and 0.71 with cross-entropy loss and MSE. The recall for SSD, YOLO, and EfficientDet under uni-loss is 0.81, 0.85, and 0.88, respectively, which is significantly higher than with MSE. In natural language processing \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **User Attention** & **Multi-Head Attention** & **Self-Attention** \\ \hline FasterRCNN & 0.80 & 0.74 & 0.68 \\ SSD & 0.83 & 0.76 & 0.71 \\ YOLO & 0.86 & 0.79 & 0.73 \\ EfficientDet & 0.89 & 0.82 & 0.80 \\ \hline Transformer & 0.78 & 0.76 & 0.71 \\ BERT & 0.80 & 0.78 & 0.71 \\ CLIP & 0.83 & 0.80 & 0.75 \\ BLIP & 0.85 & 0.82 & 0.76 \\ BLIP2 & 0.88 & 0.84 & 0.79 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy results of different attention mechanisms in baseline models. tasks, the recall for Transformer, BERT, CLIP, BLIP, and BLIP2 under uni-loss is 0.76, 0.78, 0.82, 0.86, and 0.89, respectively, which is also higher than with cross-entropy loss and MSE. This indicates that uni-loss is not only effective in protecting user privacy but also better at capturing important features in the data, enhancing recall (Table 12). From the perspective of accuracy, uni-loss once again shows its advantage. Faster-RCNN achieves an accuracy of 0.80 with uni-loss compared to 0.77 and 0.72 with cross-entropy loss and MSE. The accuracy for SSD, YOLO, and EfficientDet under uni-loss is 0.83, 0.86, and 0.89, respectively. In natural language processing tasks, the accuracy for Transformer, BERT, CLIP, BLIP, and BLIP2 under uni-loss is 0.78, 0.80, 0.83, 0.87, and 0.89, respectively, which are all higher than with the other two loss functions. These results demonstrate that uni-loss can provide higher accuracy across various tasks, which is closely related to its design of combining classification loss, privacy loss, and attention loss (Table 13). Theoretically, the advantage of uni-loss lies in its ability to simultaneously optimize model classification performance and privacy protection. By combining classification loss (cross-entropy loss), privacy loss, and attention loss, uni-loss provides a multidimensional optimization target, making the model more efficient and comprehensive in handling complex tasks. Through this comprehensive optimization, uni-loss can dynamically balance different objectives during model training, ensuring enhanced privacy protection while improving classification performance. Although cross-entropy loss performs well in classification tasks, it focuses solely on the model classification performance and does not account for privacy protection and attention mechanisms.