## 4 Results and Discussion
### Medical Image Classification Results  
The objective of this experiment was to evaluate the performance of different data protection strategies in medical image classification tasks, focusing on four key metrics: precision, recall, accuracy, and FPS. This study aimed to validate whether the proposed data obfuscation method based on probability density and information entropy could balance privacy protection with task performance. Comparisons were drawn with traditional methods, such as FHE, DP, Multi-party Computation (MPC), and FL, to provide optimized solutions for medical image analysis tasks.  
As shown in Table 3, the performance of different models varied significantly across the four evaluation metrics. FHE demonstrated strong privacy protection capabilities but suffered from high computational complexity, resulting in lower classification performance (precision: 0.82; recall: 0.79; accuracy: 0.80) and a low processing speed (FPS: 23). While DP improved processing efficiency (FPS: 25) by introducing random noise, the added noise compromised the model's classification performance (precision: 0.84; recall: 0.81; accuracy: 0.83). MPC leveraged data partitioning and distributed computation to enhance processing efficiency (FPS: 30), achieving moderate performance improvements (precision: 0.86; recall: 0.83; accuracy: 0.85). FL, which avoids direct data sharing, achieved higher classification accuracy (precision: 0.88; recall: 0.86; accuracy: 0.87) through global model aggregation, alongside significantly better processing efficiency (FPS: 45). In contrast, the proposed method achieved the best overall performance (precision: 0.93; recall: 0.89; accuracy: 0.91; FPS: 57), indicating that the data obfuscation strategy based on probability density and information entropy strikes a superior balance between privacy protection and task performance. Theoretically, the superior results of the proposed method stem from its innovative design. FHE relies on complex mathematical transformations, ensuring strong security but incurring significant computational costs, reflected in its low FPS value. DP's approach of introducing random noise provides privacy protection but mathematically diminishes the validity of the data, leading to decreased classification performance. MPC employs secret-sharing protocols to improve task performance to some extent but is limited by high communication overhead in complex scenarios. FL avoids privacy breaches through distributed data modeling, and its global model aggregation strategy significantly enhances classification performance. However, FL may encounter inconsistencies in model aggregation under highly diverse data distributions. The proposed method models the global distribution characteristics of the data through the probability density extraction module and dynamically adjusts the obfuscation intensity via the information entropy fusion module. This ensures, mathematically, that the obfuscated data maintain global consistency and preserve the core features of the original data. The design avoids FHE's high computational cost, mitigates DP's noise-induced interference, and surpasses FL's communication bottlenecks through a lightweight model architecture. Ultimately, its outstanding mathematical properties enable the proposed method to balance efficiency and accuracy in practical applications, providing new possibilities for medical image classification.