## 4 Results and Discussion
### Impact Analysis and Ablation Study on the Information Entropy Fusion Module  
The ablation study on the information entropy fusion module aimed to validate its role in privacy protection and model performance, assessing its impact on the data obfuscation intensity under different entropy control strategies, as shown in Table 5. The experiment sets up multiple comparison groups, including removing the information entropy fusion module (No Entropy Fusion), using a fixed obfuscation intensity (Fixed Obfuscation), applying random obfuscation (Random Obfuscation), and employing Shannon entropy as the entropy metric (Shannon Entropy), while comparing these against the full model (Baseline Full Model). This study primarily evaluated six key metrics: reconstruction attack success rate (RASR), Wasserstein distance, classification accuracy, F1 score, mean squared error (MSE), and computation delay. RASR reflects the security of the obfuscated data, the Wasserstein distance measures the distributional similarity between the obfuscated and original data, while accuracy, F1 score, and MSE assess the model's performance under different obfuscation strategies. The results demonstrate that the full model achieved the best balance across all metrics, indicating that the information entropy fusion module effectively adjusts the data obfuscation intensity, enhancing data security while preserving task utility as much as possible. From a mathematical perspective, these results primarily stem from the ability of the information entropy fusion module to dynamically adjust the obfuscation intensity based on data uncertainty. The superior performance of the baseline group indicates that global information entropy effectively modulates the level of obfuscation across different regions of the data, achieving an optimal trade-off between privacy protection and data utility. In contrast, removing the information entropy fusion module (No Entropy Fusion) increases the RASR to 24.7%, suggesting that obfuscation effectiveness declines, while the Wasserstein distance increases to 0.87, indicating significant deviation from the original data distribution, which, in turn, degrades classification accuracy. Fixed Obfuscation reduces the RASR but fails to adaptively adjust the obfuscation intensity, leading to insufficient protection of critical data regions and a drop in accuracy to 88.2%. Random Obfuscation performs the worst, exhibiting the highest RASR (27.1%), which suggests that arbitrary perturbations disrupt the data structure, significantly impairing task performance. The Shannon entropy approach yields suboptimal performance--while it improves over No Entropy Fusion, it still lags behind the full model, likely due to the limited generalization ability of a single entropy metric in complex data distributions. Therefore, the experimental results strongly validate the effectiveness of the information entropy fusion module, demonstrating that the \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Experiment Group** & **RASR (\%)** & **Wasserstein Dist.** & **Accuracy** & **F1 Score** & **MSE** & **Delay (s)** \\ \hline Baseline (Full Model) & 12.3 & 0.54 & 92.1 & 0.89 & 0.042 & 1.32 \\ No Entropy Fusion & 24.7 & 0.87 & 85.3 & 0.81 & 0.097 & 1.18 \\ Fixed Obfuscation & 19.8 & 0.74 & 88.2 & 0.85 & 0.063 & 1.24 \\ Random Obfuscation & 27.1 & 1.02 & 80.7 & 0.76 & 0.132 & 1.08 \\ Shannon Entropy & 14.6 & 0.60 & 91.0 & 0.87 & 0.049 & 1.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation study on the information entropy fusion module. dynamically entropy-adjusted obfuscation strategy achieves a superior balance between data security and task utility.