## 4 Results and Discussion
### Ablation Study on Different Data Obfuscation Methods  
This experiment aimed to validate the advantages of the proposed method in medical image classification and financial prediction regression tasks through an ablation study on different data obfuscation strategies. Specifically, the experiment compared obfuscation methods based on gradient information, pure Gaussian noise, and the proposed strategy based on probability density and information entropy, as shown in Figure 5. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Experiment Group** & **RASR (\%)** & **Entropy Change Rate** & **MSE** & **RMSE** & **Acc.** & **Pre.** \\ \hline Baseline (Full Model) & 11.8 & 0.67 & 0.045 & 0.212 & 91.5 & 0.88 \\ No Obfuscation & 35.7 & 0.02 & 0.032 & 0.178 & 94.3 & 0.92 \\ Fixed Obfuscation & 22.4 & 0.45 & 0.078 & 0.264 & 86.2 & 0.81 \\ Random Obfuscation & 27.1 & 0.30 & 0.093 & 0.289 & 79.6 & 0.74 \\ Gradual-Drift Adaptation & 14.2 & 0.61 & 0.048 & 0.219 & 90.8 & 0.87 \\ Sudden-Drift Adaptation & 15.9 & 0.58 & 0.052 & 0.227 & 89.6 & 0.85 \\ Recurring-Drift Adaptation & 13.5 & 0.63 & 0.046 & 0.215 & 91.2 & 0.88 \\ Mixed-Drift Adaptation & 16.7 & 0.56 & 0.057 & 0.231 & 88.9 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation study on the sensitivity of the proposed method to non-stationary distributions in time-series data. The goal was to comprehensively analyze the impact of these methods on privacy protection strength, task performance, and operational efficiency. As shown in Table 7, the gradient-based obfuscation method performed poorly in both tasks, achieving precision, recall, and accuracy of 0.69, 0.64, and 0.67, respectively, for medical image classification, with a throughput of 32 FPS. The corresponding metrics for financial prediction were 0.71, 0.67, and 0.69, with a throughput of 41 FPS. In contrast, the pure Gaussian noise method significantly improved throughput (FPS), reaching 48 and 49 for medical image classification and financial prediction, respectively. However, its disruptive effect on data distribution led to a decline in performance, weakening task-specific characteristics. The proposed method achieved the best results in both tasks, with precision, recall, and accuracy of 0.93, 0.89, and 0.91 for medical image classification and 0.95, 0.91, and 0.93 for financial prediction, while also achieving the highest FPS (57 for medical image classification and 54 for financial prediction). This demonstrates its comprehensive advantages in task performance and operational efficiency. From a theoretical perspective, the differences in experimental results stem from the varying capabilities of these obfuscation strategies to preserve data features while protecting privacy. The gradient-based obfuscation method achieves privacy protection by introducing gradient perturbations, but its reliance on task model gradient updates makes it susceptible to gradient instability and noise accumulation. This significantly weakens its ability to retain the core features of the original data, particularly in image classification and time-series prediction tasks, resulting in lower task performance. The pure Gaussian noise method relies on random sampling from a Gaussian distribution, which provides substantial privacy protection. However, the global nature of the noise disrupts the alignment between the obfuscated and original data distributions, reducing the model's ability to capture critical features. The proposed method, on the other hand, leverages the probability density extraction module to model the global distribution of the data, while the information entropy fusion module dynamically adjusts the obfuscation intensity. Mathematically, this ensures that the statistical properties of the obfuscated data align closely with those of the original data, while sensitive information is selectively protected. Additionally, the lightweight architecture of the proposed method effectively reduces