## 4 Results and Discussion
### Ablation Study on Attention Mechanism  
In this experiment, the objective was to compare the performance of different attention mechanisms within various baseline models, specifically focusing on user attention, multi-head attention, and self-attention. These comparative experiments aim to verify the advantages of user attention in terms of privacy protection and performance enhancement and to explore its application effects in both computer vision and natural language processing tasks. The precision results demonstrate that user attention consistently achieves the best performance across all models. For instance, in computer vision models, FasterRCNN achieves a precision of 0.82 with user attention compared to 0.76 and 0.70 with multi-head \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Baseline (No Privacy Preserving)** & **Our Method** & **FHE** & **FL** & **DP** & **DP + FL** \\ \hline
1 & 0.93 & 0.02 & 0.83 & 0.90 & 0.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results on throughput for different methods. attention and self-attention, respectively. The precision for SSD, YOLO, and EfficientDet models under user attention also significantly exceeds the other two mechanisms, 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 user attention is 0.81, 0.84, 0.86, 0.88, and 0.90, respectively, which are all higher than those with multi-head attention and self-attention. These results indicate that user attention can more effectively capture and protect sensitive information, improving model precision (Table 8). The recall results show a similar pattern of superiority for user attention. FasterRCNN achieves a recall of 0.79 with user attention compared to 0.73 and 0.66 with multi-head attention and self-attention. The recall for SSD, YOLO, and EfficientDet under user attention is 0.81, 0.85, and 0.88, respectively, while it is significantly lower under self-attention, at 0.70, 0.72, and 0.79. In natural language processing tasks, the recall for Transformer, BERT, CLIP, BLIP, and BLIP2 under user attention is 0.76, 0.78, 0.81, 0.84, and 0.87, respectively, and it is also higher than those with multi-head attention and self-attention. This demonstrates that user attention is not only effective in protecting user privacy but also better at capturing important features in the data, thus enhancing recall (Table 9). In terms of accuracy, user attention again shows its advantages. FasterRCNN achieves an accuracy of 0.80 with user attention compared to 0.74 and 0.68 with multi-head attention and self-attention. The accuracy for SSD, YOLO, and EfficientDet under user attention is 0.83, 0.86, and 0.89, respectively. In natural language processing tasks, the accuracy for Transformer, BERT, CLIP, BLIP, and BLIP2 under user attention is 0.78, 0.80, 0.83, 0.85, and 0.88, respectively, which are all higher than the other two mechanisms. These results show that user attention provides higher accuracy across various tasks, which is closely related to its feature of dynamically adjusting attention weights to protect user privacy. Theoretically, the advantage of user attention lies in its ability to dynamically adjust attention weights based on the characteristics of the input data and user privacy needs. With this design, user attention can effectively highlight the positions of sensitive data in the attention scores, thus \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **User Attention** & **Multi-Head Attention** & **Self-Attention** \\ \hline FasterRCNN & 0.82 & 0.76 & 0.70 \\ SSD & 0.84 & 0.78 & 0.72 \\ YOLO & 0.87 & 0.80 & 0.74 \\ EfficientDet & 0.90 & 0.84 & 0.82 \\ \hline Transformer & 0.81 & 0.77 & 0.72 \\ BERT & 0.84 & 0.79 & 0.74 \\ CLIP & 0.86 & 0.81 & 0.76 \\ BLIP & 0.88 & 0.83 & 0.78 \\ BLIP2 & 0.90 & 0.85 & 0.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Precision results of different attention mechanisms in baseline models.