## 4 Results and Discussion
### Spatial Data Testing Results  
The objective of the spatial data testing experiment was to evaluate the performance of various methods in handling spatial data, particularly in modeling high-dimensional features (e.g., images) and capturing local features in multimodal tasks. The same baselines and evaluation metrics used for the time-series dataset were applied. The experimental results revealed that the proposed method outperformed all the baseline models across all the metrics, achieving precision, recall, accuracy, and F1-score values of 0.93, 0.90, 0.92, and 0.91, respectively. These findings demonstrate the proposed method's significant advantages in extracting high-dimensional spatial features and modeling dynamic characteristics, effectively integrating local features with global dependencies and substantially improving the adaptability to complex spatial data.  
As shown in Figure 5 and Table 4, from a theoretical perspective, the performance differences among the models can be attributed to their feature modeling capabilities and algorithmic structures. Federated learning provides robust privacy protection but exhibits limited ability to model high-dimensional spatial features due to its reliance on distributed training, resulting in precision and recall values of 0.84 and 0.79, respectively, which constrained its overall performance. MPC enhances the ability to capture local features through cryptographic protocols; however, its significant communication and computation overheads limit its performance in global feature modeling. Homomorphic encryption offers end-to-end privacy protection and improves security and model performance through encrypted computation, but its computational complexity restricts its scalability for large  
Figure 4: The figure presents the violin plot of accuracy distribution for different baseline models and the proposed method in the time-series data testing results experiment.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **FPS** \\ \hline Federated Learning [42] & 0.86 & 0.81 & 0.84 & 0.83 & 29 \\ MPC [43] & 0.88 & 0.85 & 0.87 & 0.86 & 34 \\ Homomorphic Encryption [44] & 0.90 & 0.87 & 0.89 & 0.88 & 37 \\ TEE-Based [44] & 0.92 & 0.89 & 0.91 & 0.90 & 40 \\ Proposed Method & 0.95 & 0.91 & 0.93 & 0.93 & 46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of time-series data testing.  
scale spatial data, with recall reaching only 0.84. TEE-based methods significantly enhance computational efficiency and global modeling capability through hardware isolation technologies, achieving superior results across all the metrics compared to the aforementioned methods. However, due to weaker capture of complex local features, the F1-score remained at 0.89. In contrast, the proposed method employs a sliding-window attention mechanism to effectively capture local spatial information, complemented by a stripe-cross attention mechanism to extract global features, mathematically optimizing the feature representation capabilities. Additionally, the sliding loss function further reinforces the joint modeling of local features and global patterns during training, enabling the proposed method to achieve higher precision and robustness in complex spatial data tasks. These results underscore the innovation in mathematical structure and computational design of the proposed method, highlighting its applicability and superiority in multimodal spatial data scenarios.