## 1 Introduction  
Secure computation has emerged as a critical research direction in modern computer science and financial technology, with its applications expanding across financial forecasting, natural language processing (e.g., financial text analysis), autonomous driving prediction, low-altitude economy, and medical imaging [1]. The importance of data in these domains is self-evident. For instance, in the financial sector, precise stock price forecasting directly influences investment strategies and the stable operation of financial markets [2]. In autonomous driving, vehicle path prediction and behavior analysis rely heavily on complex multimodal data processing to ensure driving safety and system reliability [3, 4, 5]. Similarly, tasks such as drone logistics and airspace monitoring in low-altitude economic scenarios demand robust data collaboration and privacy protection mechanisms [6]. In medical imaging analysis, lesion detection and diagnostic assistance depend on high-quality medical data, which must be efficiently analyzed while protecting patient privacy [7; 8; 9]. These scenarios collectively highlight a central challenge: how to balance computational efficiency with data privacy in large-scale data analysis, which serves as a driving force for advancements in secure computation technologies. Traditional data processing methods predominantly employ centralized architectures, where data are stored on a central server for analysis and computation [10]. However, such centralized approaches have significant limitations. First, centralized storage increases the risk of privacy breaches, particularly for sensitive financial data or medical information, where any oversight can result in severe security issues [11]. Second, as data scales continue to grow, the traditional methods exhibit performance bottlenecks in handling high-dimensional data and complex tasks, failing to meet the demands for efficiency and real-time processing [12]. While distributed computing partially alleviates these issues, challenges such as high communication overhead and complex model synchronization in large-scale collaborative computing remain barriers to replacing traditional methods entirely. In recent years, secure computation technologies have emerged as key solutions to these challenges, with federated learning, secure multi-party computation, and homomorphic encryption being the three core approaches. Federated learning enables collaborative model training across different data owners without sharing raw data, significantly reducing privacy risks through distributed training [13]. Secure multi-party computation employs advanced cryptographic protocols, allowing multiple parties to perform collaborative computations without exposing their data, which is critical for cross-institutional data analysis [14; 15]. Homomorphic encryption, on the other hand, allows computations to be performed directly on encrypted data, offering end-to-end privacy protection from input to output [16]. However, applying deep learning methods to secure computation scenarios presents notable challenges [17]. First, deep learning typically relies on large-scale data and complex computations, which conflict with the high computational overhead of privacy-preserving techniques. Second, the existing deep learning methods often model different modalities separately, lacking effective mechanisms for collaborative modeling, especially when integrating time-series data (e.g., stock prices and text) with spatial data (e.g., images and medical scans), resulting in limited performance [18]. Privacy protection in cloud computing has garnered significant attention in recent years. With the increasing demand for large-scale data sharing and computation, traditional privacy-preserving methods face challenges in security, computational efficiency, and scalability.