high-frequency trading data could lead to market manipulation, creating instability in the entire financial system [10]. In the field of economic data protection, the economic operation data of enterprises and nations serve as a crucial basis for decision making [11]. If these data are leaked, they could be exploited by competitors, weakening market competitiveness. At the national level, the leakage of economic data could become a strategic tool for adversaries, threatening national economic security [12]. Therefore, how to effectively protect data security during data sharing and analysis has become a key focus for society [5]. The development of data protection technologies not only ensures the privacy and security of individuals but also provides important support for the healthy development of industries, further promoting the deep exploration and application of data value [13]. In the realm of data protection technologies, traditional solutions mainly fall into two categories: encryption-based methods and data obfuscation-based methods [14]. Fully Homomorphic Encryption (FHE) [15] is an encryption method with theoretical completeness that allows computations on encrypted data without decrypting it [16]. This technique ensures high data security through mathematical transformations, representing a significant breakthrough in protecting data privacy. However, the computational overhead of FHE is extremely high, making it unsuitable for practical applications [17]. For instance, in real-time medical data analysis, the high latency of FHE could fail to meet clinical diagnosis time requirements. Differential privacy (DP) [18] is a widely applied data privacy protection technique that introduces noise into the data to protect individual privacy [19]. However, a major issue with DP is that the introduction of noise inevitably impacts the data's effectiveness, resulting in a decrease in the accuracy of data analysis [20]. For example, in financial forecasting, DP may obscure key details of stock price fluctuations, thus reducing the performance of prediction models. In summary, traditional encryption and obfuscation techniques have a clear conflict between data security and system performance: encryption methods offer high security but poor performance, while obfuscation methods achieve better performance at the cost of some security. This conflict is particularly prominent in modern application scenarios, necessitating new technologies to balance both. In recent years, with the rapid development of deep learning technologies, deep learning-based models have gradually become an important tool in data protection [21; 22]. Deep learning has demonstrated powerful capabilities in feature extraction, pattern recognition, and complex task optimization, providing new ideas for data security protection [23; 24; 25]. [PERSON] et al. [26] proposed a deep learning- and big data-based framework to enhance the security of IoT devices, particularly in terms of adaptability and response speed when facing various network attacks, achieving an accuracy of 98.7%, a precision of 97.5%, and a recall of 96.2%. [PERSON] et al. [27] proposed several deep learning-based network security protection methods aimed at enhancing the defense capabilities and response speeds of systems in smart cities (e.g., traffic, energy, and healthcare) when facing cyber-attacks. Their approach achieved an accuracy of 95.4%, a false positive rate of 2.3%, and an average response time of 0.45 s. However, it was noted that deep learning model training requires a large amount of labeled data and may not perform efficiently on devices with limited computational resources. [PERSON] et al. [28] introduced a new IoT network security protection method by combining federated learning and deep learning, enabling training on multiple IoT nodes without data centralization, thus protecting user privacy. The method showed a detection accuracy of 97.3%, with training efficiency improved by 20% and communication overhead reduced by 15%.