This paper introduces a user-centered data privacy protection framework utilizing large language models (LLMs) and user attention mechanisms, which are tailored to address urgent privacy concerns in sensitive data processing domains like financial computing and facial recognition. The innovation lies in a novel user attention mechanism that dynamically adjusts attention weights based on data characteristics and user privacy needs, enhancing the ability to identify and protect sensitive information effectively. Significant methodological advancements differentiate our approach from existing techniques by incorporating user-specific attention into traditional LLMs, ensuring both data accuracy and privacy. We succinctly highlight the enhanced performance of this framework through a selective presentation of experimental results across various applications. Notably, in computer vision, the application of our user attention mechanism led to improved metrics over traditional multi-head and self-attention methods: FasterRCNN models achieved precision, recall, and accuracy rates of 0.82, 0.79, and 0.80, respectively. Similar enhancements were observed with SSD, YOLO, and EfficientDet models with notable increases in all performance metrics. In natural language processing tasks, our framework significantly boosted the performance of models like Transformer, BERT, CLIP, BLIP, and BLIP2, demonstrating the framework’s adaptability and effectiveness. These streamlined results underscore the practical impact and the technological advancement of our proposed framework, confirming its superiority in enhancing privacy protection without compromising on data processing efficacy.

This paper developed a radish disease detection system based on a hybrid attention mechanism, significantly enhancing the precision and real-time performance in identifying disease characteristics. By integrating spatial and channel attentions, this system demonstrated superior performance across numerous metrics, particularly achieving 93% precision and 91% accuracy in detecting radish virus disease, outperforming existing technologies. Additionally, the introduction of the hybrid attention mechanism proved its superiority in ablation experiments, showing higher performance compared to standard self-attention and the convolutional block attention module. The study also introduced a hybrid loss function that combines cross-entropy loss and Dice loss, effectively addressing the issue of class imbalance and further enhancing the detection capability for rare diseases. These experimental results not only validate the effectiveness of the proposed method, but also provide robust technical support for the rapid and accurate detection of radish diseases, demonstrating its vast potential in agricultural applications. Future research will continue to optimize the model structure and computational efficiency to accommodate a broader range of agricultural disease detection needs.

This paper addresses the critical challenge of secure computing in the context of deep learning, focusing on the pressing need for effective data privacy protection during transmission and storage, particularly in sensitive fields such as finance and healthcare. To tackle this issue, we propose a novel deep learning model that integrates a hash tree structure with a skip attention mechanism. The hash tree is employed to ensure data integrity and security, enabling the rapid verification of data changes, while the skip attention mechanism enhances computational efficiency by allowing the model to selectively focus on important features, thus minimizing unnecessary processing. The primary objective of our research is to develop a secure computing model that not only safeguards data privacy but also optimizes feature extraction capabilities. Our experimental results on the CIFAR-10 dataset demonstrate significant improvements over traditional models, achieving a precision of 0.94, a recall of 0.89, an accuracy of 0.92, and an F1-score of 0.91, notably outperforming standard self-attention and CBAM. Additionally, the visualization of results confirms that our approach effectively balances efficient feature extraction with robust data privacy protection. This research contributes a new framework for secure computing, addressing both the security and efficiency concerns prevalent in current methodologies.

A deep reinforcement learning framework is presented for strategy generation and profit forecasting based on large-scale economic behavior data. By integrating perturbation-based augmentation, backward return estimation, and policy-stabilization mechanisms, the framework facilitates robust modeling and optimization of complex, dynamic behavior sequences. Experimental evaluations on four distinct behavior data subsets indicate that the proposed method achieved consistent performance improvements over representative baseline models across key metrics, including total profit gain, average reward, policy stability, and profit–price correlation. On the sales feedback dataset, the framework achieved a total profit gain of 0.37, an average reward of 4.85, a low-action standard deviation of 0.37, and a correlation score of $R^2=0.91$. In the overall benchmark comparison, the model attained a precision of 0.92 and a recall of 0.89, reflecting reliable strategy response and predictive consistency. These results suggest that the proposed method is capable of effectively handling decision-making scenarios involving sparse feedback, heterogeneous behavior, and temporal volatility, with demonstrable generalization potential and practical relevance.