## 5 Conclusions  
As the demand for data privacy protection continues to grow, balancing privacy preservation and task performance has become a significant challenge in data analysis. Traditional methods such as FHE, DP, MPC, and FL have achieved notable success in privacy protection and data utility. However, these approaches face significant limitations, including high computational complexity, noise interference, and communication overhead. To address these issues, this paper proposes a novel data obfuscation method based on probability density and information entropy, with a design that directly addresses the trade-offs observed in traditional techniques. By integrating a probability density extraction module and an information entropy fusion module, the method models the global distribution of data and dynamically adjusts the obfuscation intensity. Additionally, a fusion loss function is designed to optimize the balance between privacy protection and task performance. Experimental results provide strong evidence for the method's effectiveness. In medical image classification, the proposed method achieved precision, recall, and accuracy of 0.93, 0.89, and 0.91, respectively, with a throughput of 57 FPS, demonstrating its ability to preserve data utility while ensuring privacy. Compared to FHE (precision: 0.82; throughput: 23 FPS) and DP (precision: 0.84; throughput: 25 FPS), the method achieved clear improvements by reducing computational overhead and noise-induced performance degradation. Similarly, in financial prediction tasks, the proposed method exhibited excellent capability in capturing temporal features, achieving precision, recall, and accuracy of 0.95, 0.91, and 0.93, with a throughput of 54 FPS, far surpassing traditional methods. These results highlight not only the superior performance of the proposed modules but also their ability to adapt to different types of data, such as medical images and time-series data, effectively addressing privacy and utility concerns in diverse scenarios. Furthermore, ablation experiments provided detailed insights into the contributions of individual components, such as the probability density extraction module and the information entropy fusion module, to the overall system performance. Specifically, the experiments confirmed that the dynamic adjustment of the obfuscation intensity significantly enhances privacy protection while maintaining task relevance. Compared with gradient-based obfuscation and pure Gaussian noise methods, the proposed method preserves statistical characteristics more effectively, ensuring that the obfuscated data remain analytically valid for downstream tasks. In conclusion, the integration of probability density modeling and dynamic obfuscation adjustment offers a robust framework for balancing privacy and performance. The superior experimental results, supported by ablation studies and comparative analyses, underscore the effectiveness of this approach in real-world applications. These findings lay a solid foundation for extending the proposed method to other privacy-sensitive domains while further optimizing its efficiency and scalability. Conceptualization, H.C., C.Q., L.C., M.R. and C.L. ; Data curation, S.L., X.Z., H.W. and C.L. ; Formal analysis, J.X. ; Funding acquisition, C.L. ; Investigation, S.L. ; Methodology, H.C., C.Q., L.C. and C.L. ; Resources, S.L., X.Z. and H.W. ; Software, H.C., C.Q., L.C., J.X. and M.R. ; Supervision, M.R. ; Validation, J.X. ; Visualization, X.Z. and H.W. ; Writing--original draft, H.C., C.Q., L.C., J.X., S.L., X.Z., H.W. and M.R. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China under grant number 61202479. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author.