## 4 Results and Discussion  
### Results on Computer Vision Tasks  
The purpose of the experimental design in this study is to evaluate the performance of the proposed user-centered data privacy protection framework across different computer vision tasks. By comparing the performance of various models using different privacy protection methods, the goal is to verify whether our method can maintain or even enhance the precision, recall, and accuracy of these models while protecting user privacy. This is crucial for practical applications because only by ensuring high model performance while protecting privacy can the framework be widely applied in highly sensitive data processing fields such as financial computing and facial recognition, as shown in Tables 1-3. From the experimental results, it is evident that models applying our method significantly outperform other privacy protection methods in terms of precision, recall, and accuracy. Specifically, FasterRCNN achieves a precision of 0.82 with our method, compared to 0.77 with FHE, 0.74 with federated learning (FL), 0.73 with DP, and 0.71 with DP + FL. Similar trends are observed with SSD, YOLO, and EfficientDet, with EfficientDet achieving a precision of 0.90, which is significantly higher than other methods. These results can be analyzed from the mathematical characteristics of the models. FasterRCNN uses a Region Proposal Network (RPN) to generate candidate regions and processes these through a two-stage approach (first generating region proposals, then classification and regression). The precision improvement is mainly attributed to the integration of privacy protection and user attention mechanisms in our method, ensuring high-precision detection while protecting privacy. SSD performs detection on feature maps at different scales, and its precision and recall improvements are due to the combination of differential privacy techniques and user-centered mechanisms in our method, allowing the dynamic adjustment of attention on multi-scale feature maps to protect sensitive information while enhancing detection performance. YOLO, as a single-stage detection model, achieves fast speed and high precision, with precision increasing to 0.87 and recall to 0.85. This is primarily because our user attention mechanism can accurately identify and protect sensitive information in a single forward pass while maintaining efficiency and accuracy. EfficientDet optimizes network structure and parameters, particularly in the application of BiFPN (Bidirectional Feature Pyramid Network), enabling high efficiency while maintaining high precision and recall. Our method further combines differential privacy protection by adding noise at each layer, protecting user data and demonstrating superior performance in practical applications. Theoretically, the performance improvement observed in different models applying our method can be attributed to several factors. Firstly, the user attention mechanism dynamically adjusts attention weights, enabling the model to precisely identify and protect sensitive information based on the characteristics of the input data and user privacy needs. Secondly, differential privacy techniques effectively prevent data leakage during training and inference by adding noise to parameter updates. The combination of these techniques enhances the model's privacy protection capabilities while ensuring efficient \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Proposed Method** & **FHE** & **FL** & **DP** & **DP + FL** \\ \hline FasterRCNN & 0.82 & 0.77 & 0.74 & 0.73 & 0.71 \\ SSD & 0.84 & 0.78 & 0.76 & 0.74 & 0.72 \\ YOLO & 0.87 & 0.81 & 0.79 & 0.77 & 0.75 \\ EfficientDet & 0.90 & 0.83 & 0.81 & 0.79 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of precision on computer vision tasks for different methods.