However, the method's performance may be suboptimal in high-latency network environments, and it still faces challenges with computational and communication loads on resource-constrained devices. [PERSON] et al. [29] proposed a deep learning-based multimedia security solution encompassing identity authentication, encryption,and information hiding to enhance multimedia data security. The experimental results showed that the authentication accuracy was 98.2%, the encryption speed was 200 MB/s, and the information hiding robustness was 95.7%. However, the method depends on high computational resources and may face performance bottlenecks when processing high-resolution data. [PERSON] et al. [30] studied the security threat posed by adversarial samples to deep learning models for COVID-19, proposing protection methods to enhance model robustness. The experiments showed that the original model had an accuracy of 95.6%, but when subjected to adversarial attacks, the accuracy dropped to 68.3%. This method increases computational overhead and may not be effectively implemented on resource-limited devices. Deep learning models can learn the semantic features of data and abstract them into higher-level representations. This abstraction significantly reduces the risk of direct leakage of original data [31]. Despite the numerous tools that deep learning offers for data security, its application in privacy protection still faces challenges. For instance, deep learning models themselves may become a vector for privacy leakage (such as model inversion attacks), and the high computational costs in both model training and inference still limit their large-scale application in practical scenarios. In response to these challenges, a data obfuscation security framework based on probability density and information entropy is proposed. By combining the feature extraction of probability distributions and the dynamic control of information complexity, this framework achieves a balance between data security and system performance. Compared to existing methods, the innovations of this framework include the following aspects:  
* Probability density extraction module: This module is designed to extract probability distribution characteristics from raw data. Compared to traditional random noise methods, it can more accurately capture data characteristics, thereby reducing the performance loss in downstream tasks. * Information entropy fusion module: This module quantifies the data's complexity using information entropy and dynamically adjusts the strength of data obfuscation by combining Shannon entropy and conditional entropy. It can effectively protect sensitive information while ensuring the preservation of core data features. * Multi-scenario validation: The framework's generality and effectiveness are validated in two typical application scenarios, healthcare and finance. Experiments demonstrate that the proposed method outperforms existing approaches in terms of accuracy, throughput, and security. * Balance between performance and security: The proposed method significantly improves system efficiency while achieving efficient obfuscation, reducing the computational overhead of encryption and decryption processes and making it suitable for real-time data analysis scenarios. In conclusion, the proposed method provides a new approach to data protection across multiple scenarios by balancing data security and performance.