## 4 Results and Discussion
### Impact Analysis and Ablation Study on the Information Entropy Fusion Module
Ablation Study on the Sensitivity of the Proposed Method to Non-Stationary Distributions in Time-Series Data  
This experiment aimed to evaluate the stability of the proposed information entropy fusion-based obfuscation method under non-stationary time-series data conditions (i.e., concept drift), particularly in cases where data distribution undergoes gradual changes, sudden shifts, or periodic variations. The adaptability of the obfuscation strategy and its impact on both privacy protection and task performance were assessed. The experiment utilized financial time-series data and synthetic time-series data, simulating real-world applications through various types of concept drift. A systematic comparison was conducted between the full information entropy fusion model (Baseline), a fixed obfuscation strength (Fixed Obfuscation), no obfuscation (No Obfuscation), and random obfuscation (Random Obfuscation) to analyze the effectiveness of the information entropy fusion module. Different types of concept drift were simulated to examine the adaptability of the obfuscation strategy. Gradual Drift: The data distribution changes progressively over time, such as slow market trends rising or declining. Sudden Drift: A drastic change in data occurs at a specific time point, such as a black swan event in financial markets. Recurring Drift: The data distribution follows a periodic pattern, such as economic cycles or market fluctuations. Mixed Drift: A combination of gradual and sudden drift to simulate complex market dynamics, where long-term trends coexist with short-term disruptions. The experimental results in Table 6 indicate that the full information entropy fusion model (Baseline) maintained a low reconstruction attack success rate of 11.8% and a high entropy change rate of 0.67 across all concept-drift scenarios, demonstrating that the method effectively adjusted the obfuscation intensity dynamically and preserved strong privacy protection, even when the data distribution shifted. Additionally, the baseline model achieved near-optimal performance in regression tasks (MSE = 0.045; RMSE = 0.212) and classification tasks (accuracy = 91.5%; precision = 0.88; recall = 0.86), indicating that it maintained predictive performance while ensuring privacy. In contrast, the fixed obfuscation strategy (Fixed Obfuscation), due to its lack of adaptation to time-series distribution drift, experienced an increase in RASR to 22.4% and a drop in the entropy change rate to 0.45, reducing privacy protection effectiveness, while its classification and regression performance also degraded (MSE = 0.078; accuracy = 86.2%). The random obfuscation strategy (Random Obfuscation) further exhibited instability, with the highest RASR (27.1%) and a significant drop in task performance (accuracy = 79.6%), indicating that random perturbations failed to maintain the statistical consistency of the data, thereby impairing the model's learning ability. The no-obfuscation strategy (No Obfuscation) achieved the best task performance (accuracy = 94.3%) but exhibited the highest RASR (35.7%), posing a severe privacy leakage risk. From a mathematical perspective, the superiority of the baseline model stems from the dynamic adjustment mechanism of the information entropy fusion module, where the obfuscation parameter \(\alpha_{t}\) is adaptively regulated based on the time-series data entropy \(H_{t}\). This ensures that the level of data obfuscation is adjusted in response to changes in the statistical properties of the data. In the gradual-drift scenario, this mechanism continuously adapts \(\alpha_{t}\) to match long-term trend changes. In sudden-drift scenarios, abrupt entropy changes allow for the rapid adjustment of the obfuscation intensity, effectively countering drastic distribution shifts. In recurring-drift scenarios, the entropy metric captures periodic patterns in the data, balancing privacy protection and task performance.