Jiaqi Shi’s research while affiliated with China Agricultural University and other places

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.

This paper proposes a data security training framework based on symmetric projection space and adversarial training, aimed at addressing the issues of privacy leakage and computational efficiency encountered by current privacy protection technologies when processing sensitive data. By designing a new projection loss function and combining autoencoders with adversarial training, the proposed method effectively balances privacy protection and model utility. Experimental results show that, for financial time-series data tasks, the model using the projection loss achieves a precision of 0.95, recall of 0.91, and accuracy of 0.93, significantly outperforming the traditional cross-entropy loss. In image data tasks, the projection loss yields a precision of 0.93, recall of 0.90, accuracy of 0.91, and mAP@50 and mAP@75 of 0.91 and 0.90, respectively, demonstrating its strong advantage in complex tasks. Furthermore, experiments on different hardware platforms (Raspberry Pi, Jetson, and NVIDIA 3080 GPU) show that the proposed method performs well on low-computation devices and exhibits significant advantages on high-performance GPUs, particularly in terms of computational efficiency, demonstrating good scalability and efficiency. The experimental results validate the superiority of the proposed method in terms of data privacy protection and computational efficiency.