A Secure and Efficient Framework for Multimodal Prediction Tasks in Cloud Computing with Sliding-Window Attention Mechanisms

An efficient and secure computation framework based on the sliding-window attention mechanism and sliding loss function was proposed to address challenges in temporal and spatial feature modeling for multimodal data processing. The framework aims to overcome the limitations of traditional methods in privacy protection, feature-capturing capabilities, and computational efficiency. The experimental results demonstrated that, in time-series data processing tasks, the proposed method achieved precision, recall, accuracy, and F1-score values of 0.95, 0.91, 0.93, and 0.93, respectively, significantly outperforming the federated learning, secure multi-party computation, homomorphic encryption, and TEE-based approaches. In spatial data processing tasks, these metrics reached 0.93, 0.90, 0.92, and 0.91, also surpassing all the comparative methods. Compared with the existing secure computation frameworks, the proposed approach substantially enhanced computational efficiency while minimizing accuracy loss, all while ensuring data privacy. These findings provide an efficient and reliable solution for privacy protection and data security in cloud computing environments. Furthermore, the research demonstrates significant theoretical value and practical potential in real-world scenarios such as financial forecasting and image analysis.