## 4 Results and Discussion
### Image Data Classification Results  
The primary objective of this experiment is to evaluate the performance of the proposed model across various datasets, including Brain CT, CIFAR-10 (Canadian Institute for Advanced Research), and ImageNet, to comprehensively validate metrics such as precision, recall, memory consumption, and frames per second (FPS). In this experimental design, these metrics enable a detailed comparison of our proposed method with classical deep learning models across different tasks, assessing its adaptability and resource efficiency in image recognition tasks. Tables 3-5 and Figure 9 showcase the versatility and efficiency of our approach in multi-task scenarios. By selecting diverse datasets (e.g., Brain CT representing medical data and CIFAR-10 and ImageNet as standard computer vision data), we can observe the model's stability and performance across varying data structures and complexities, especially in applications focused on privacy protection and information extraction for sensitive data. The experimental results show that the proposed model achieves high precision and F1 scores across all datasets, performing particularly well on the Brain CT dataset with a precision of 0.92 and an accuracy of 0.91, surpassing classic models like AlexNet, GoogleNet, ResNet, and EfficientNet. However, our model also demonstrates relatively higher memory consumption and lower FPS. Due to its use of multi-head attention mechanisms and hash tree structures, the model excels in accurately extracting and safeguarding critical features within image data, which is especially suitable for high-precision tasks, such as  
Figure 8: ROCs of different models. detecting subtle lesions in medical imaging. Although this adds to memory usage and results in reduced FPS, this design proves advantageous in applications that prioritize high precision and data privacy. On CIFAR-10 and ImageNet, which are standard computer vision datasets, our method achieves precision and F1 scores close to those of EfficientNet but with higher memory usage. Theoretically, this performance reflects the structural complexity of our method, allowing for more effective multi-level feature extraction when handling high-dimensional image data. As such, our model performs exceptionally well in scenarios with complex dependencies, making it well suited for high-stakes tasks requiring privacy protection and precise recognition, such as medical diagnostics. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **Memory (MB)** & **FPS** \\ \hline AlexNet [42] & 0.98 & 0.96 & 0.97 & 0.97 & 238 & 57 \\ Inception v1[43] & 0.98 & 0.97 & 0.98 & 0.97 & 27 & 52 \\ ResNet50 [44] & 0.99 & 0.98 & 0.98 & 0.98 & 98 & 47 \\ EfficientNet-B0 [45] & 0.99 & 0.99 & 0.99 & 0.99 & 27 & 38 \\ Proposed Method & 0.99 & 0.99 & 0.99 & 0.99 & 385 & 26 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of various models on the CIFAR-10 dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **Memory (MB)** & **FPS** \\ \hline AlexNet [42] & 0.81 & 0.78 & 0.80 & 0.79 & 240 & 58 \\ Inception v1[43] & 0.84 & 0.81 & 0.83 & 0.82 & 27 & 52 \\ ResNet50 [44] & 0.87 & 0.85 & 0.86 & 0.86 & 98 & 49 \\ EfficientNet-B0 [45] & 0.90 & 0.88 & 0.89 & 0.89 & 29 & 37 \\ Proposed Method & 0.92 & 0.89 & 0.91 & 0.90 & 381 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of various models on the Brain CT dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **Memory (MB)** & **FPS** \\ \hline AlexNet [42] & 0.98 & 0.96 & 0.97 & 0.97 & 238 & 57 \\ Inception v1[43] & 0.98 & 0.97 & 0.98 & 0.97 & 27 & 52 \\ ResNet50 [44] & 0.99 & 0.98 & 0.98 & 0.98 & 98 & 47 \\ EfficientNet-B0 [45] & 0.99 & 0.99 & 0.99 & 0.99 & 27 & 38 \\ Proposed Method & 0.99 & 0.99 & 0.99 & 0.99 & 385 & 26 \\ \hline \hline \end{tabular}
\end{table}