## 1 Introduction
Beyond federated learning (FL), secure multi-party computation (MPC), homomorphic encryption (HE), and trusted execution environment (TEE)-based approaches, several recent innovations have emerged in the field of privacy-preserving computation. For instance, differential privacy (DP) techniques have been widely applied in cloud-based data analytics. DP operates by introducing controlled noise into statistical computations to limit the influence of individual data points on the final results, thereby reducing the risk of data leakage [19]. In recent years, deep-learning-based differential privacy methods, such as DP-SGD, have been proposed to protect data privacy during the training process and have shown promising results across multiple domains [20]. Additionally, privacy-preserving techniques based on secure hardware have seen significant advancements. Hybrid approaches that combine trusted execution environments (TEEs) with homomorphic encryption have been developed to enhance computational efficiency while maintaining data security. These approaches have been effectively applied in privacy-preserving computation and blockchain applications [21]. Moreover, blockchain-based privacy protection schemes leverage decentralized architectures and zero-knowledge proofs (ZKPs) to enable secure computations without relying on trusted third parties, making them particularly suitable for multi-party collaborative cloud computing scenarios [22]. Notably, recent studies have proposed novel privacy-preserving machine learning frameworks, such as privacy-aware methods based on graph neural networks (GNNs) [23], which have demonstrated strong privacy protection capabilities in social network analysis and financial risk assessment. Furthermore, privacy-enhanced methods that integrate federated learning with knowledge distillation (KD) have been introduced to reduce communication overhead and improve model performance [24]. To address these issues, a high-efficiency secure computation framework based on sliding-window attention is proposed in this study. By optimizing attention mechanisms and multimodal data fusion techniques, this framework enhances computational efficiency and predictive accuracy while ensuring privacy protection. The core innovation lies in the introduction of the sliding-window attention mechanism, which segments input data and employs sliding windows to capture both local and global dependencies, significantly reducing computational complexity and improving predictive performance. Additionally, a temporal-spatial fusion module is designed to enable collaborative modeling of time-series and spatial data, enhancing the framework's ability to process multimodal data effectively. This enables the framework to excel in applications such as financial forecasting, autonomous driving, and medical imaging. A novel sliding loss function is also developed to improve the model's adaptability to dynamic data variations, further optimizing the convergence speed and model performance during training. The framework's efficacy is validated through extensive experiments conducted in representative scenarios including finance, autonomous driving, and medical imaging. The experimental results demonstrate that, compared to existing methods such as federated learning, secure multi-party computation, and homomorphic encryption, the proposed framework achieves superior privacy protection, computational efficiency, and predictive accuracy. These findings suggest that the proposed sliding-window attention-based secure computation framework offers a novel solution for privacy-preserving and efficient multimodal data processing while providing a theoretical and practical foundation for advancing secure computation research in other domains. Future work will focus on extending and applying this framework to more complex and diverse scenarios.