## 3 Materials and Methods
### Proposed Method  
#### 3.3.1 Overview  
A user-centered data privacy protection framework based on LLM and user attention mechanisms is proposed in this paper. This framework is designed to protect users' sensitive information while ensuring efficient and accurate data processing, as shown in Figure 2.  
Figure 2: Overview of proposed method.  
The entire approach comprises several key modules: user-centered LLM, user attention mechanism, and uni-loss. These modules are interconnected to collectively achieve the goals of data privacy protection and data processing. The model design begins with preprocessed data, proceeding through the following steps: model input layer, privacy protection layer, core computation layer, and output layer. The user-centered LLM is the core component of this framework, which is responsible for processing and analyzing user data. After data preprocessing, the prepared data are input into the user-centered LLM for further processing. The primary function of the user-centered LLM is to protect user privacy during model training and inference by incorporating differential privacy techniques. The detailed processes of each module are as follows:  
1. Model input layer: Preprocessed data first enter the model input layer. Here, text data and image data are processed separately. For text data, the input layer converts it into word vectors or sentence vectors; for image data, the input layer converts it into pixel matrices or feature vectors.
2. Privacy protection layer: After the model input layer, data enters the privacy protection layer. The main task of this layer is to add noise through differential privacy techniques to protect user privacy. Specifically, given a set of model parameters \(\theta\), differential privacy is achieved by adding noise \(\mathcal{N}(0,\sigma^{2})\) to the parameters with the mathematical expression as follows: \[\theta^{\prime}=\theta+\mathcal{N}(0,\sigma^{2})\] (10) where \(\mathcal{N}(0,\sigma^{2})\) represents Gaussian noise with mean 0 and variance \(\sigma^{2}\). This approach effectively prevents user data from being leaked during transmission and processing.
3. Core computation layer: After the privacy protection layer, data enter the core computation layer, which consists of multiple neural network layers (e.g., Transformer architecture) responsible for deep processing and analysis of the data. In this layer, the model uses a multi-head attention mechanism to capture data features. The multi-head attention mechanism is calculated as follows: \[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V\] (11) where \(Q\), \(K\), and \(V\) represent the query, key, and value matrices, respectively, and \(d_{k}\) is the dimension of the keys. This mechanism allows the model to compute multiple self-attentions in parallel, capturing different features of the data.
4. User attention mechanism: During the core computation layer process, the user attention mechanism is introduced. This mechanism dynamically adjusts attention weights to ensure that the model can protect users' sensitive information while processing  
To optimize model performance and privacy protection, a unified loss function (uniloss) is designed in this paper. The uni-loss combines classification loss, privacy loss, and attention loss to ensure optimal performance across multiple objectives. In summary, the proposed user-centered data privacy protection framework achieves the efficient and precise protection of user privacy in various application scenarios through the collaborative work of the user-centered LLM, user attention mechanism, and uni-loss. This framework provides a new solution for the field of data privacy protection.