## 3 Materials and Methods
### Proposed Method
#### 3.3.3 Autoencoder-Based Data Generation Module  
The autoencoder-based data generation module plays a crucial role in the privacy protection framework proposed in this study. Autoencoders effectively separate and reconstruct sensitive and nonsensitive information by encoding the input data into a low-dimensional latent representation and then generating obfuscated data through the decoder, thereby providing efficient data privacy protection. Unlike traditional attention mechanisms, which focus on weighting features to highlight important information, autoencoders rely primarily on the encoding and decoding process to compress and reconstruct data, as shown in Algorithm 1. ```
Input: Input data \(\mathbf{X}\), Discriminator \(D\), Generator \(G\), Number of epochs \(E\), Learning rate \(\alpha\) Output: Optimized Generator and Discriminator
1 forepoch\(\gets 1\) to \(E\)do
2 Sample a batch of real data \(\mathbf{X}_{\text{real}}\) from dataset;
3 Generate a batch of fake data \(\mathbf{X}_{\text{fake}}\gets G(\mathbf{Z})\);
4 Update Discriminator;
5 Calculate discriminator loss \(L_{D}\leftarrow\mathbb{E}[\log D(\mathbf{X}_{\text{real}})]+\mathbb{E}[\log(1-D( \mathbf{X}_{\text{fake}}))]\);
6 Update \(D\) using \(L_{D}\);
7 Update Generator;
8 Calculate generator loss \(L_{G}\leftarrow\mathbb{E}[\log(1-D(\mathbf{X}_{\text{fake}}))]\);
9 Update \(G\) using \(L_{G}\);
```  
**Algorithm 1**Autoencoder-Based Data Generation Module Training  
In privacy protection tasks, the autoencoder-based data generation module offers a distinct advantage, as it can accurately capture key features in sensitive data through learned network weights while avoiding unnecessary privacy leakage. The specific network structure is as follows:  
1. Encoder design: The encoder consists of several fully connected layers designed to map the input data \(\mathbf{X}\) to a low-dimensional latent space \(\mathbf{H}\). Assume the input data \(\mathbf{X}\) have dimension \(d\), and the encoder's output \(\mathbf{H}\) has dimension \(k\) (typically, \(k\ll d\)). The first layer of the encoder maps the input data to an intermediate layer \(h_{1}\) using a fully connected layer, with parameters \(W_{1}\in\mathbb{R}^{d\times h_{1}}\), and bias \(b_{1}\in\mathbb{R}^{h_{1}}\), using the ReLU activation function. This process continues, progressively compressing the data features down to the latent space \(\mathbf{H}\in\mathbb{R}^{n\times k}\). 2. Decoder design: The decoder's task is to reconstruct the original data \(\mathbf{X}^{\prime}\) from the latent representation \(\mathbf{H}\). The structure of the decoder is similar to the encoder and consists of several fully connected layers. Assume that the final output of the decoder is the reconstructed data \(\mathbf{X}^{\prime}\in\mathbb{R}^{n\times d}\). The first layer of the decoder maps the latent representation \(\mathbf{H}\) back to the high-dimensional space: \[\mathbf{X}^{\prime}=\text{Sigmoid}(\mathbf{H}W_{\text{out}}+b_{\text{out}}),\] (23) where \(W_{\text{out}}\in\mathbb{R}^{k\times d}\) is the weight matrix of the decoder, \(b_{\text{out}}\in\mathbb{R}^{d}\) is the bias, and the activation function is Sigmoid (or ReLU, depending on the task requirements), ensuring that the reconstructed data maintain the structure of the original data. 3. Network parameter design: For each layer of the network, careful consideration is given to the matching of input and output dimensions and the network's expressiveness. The encoder typically uses intermediate layers \(h_{1},h_{2},\dots,h_{n}\), where \(h_{1}\) is the output of the first layer, progressively increasing the feature abstraction capacity until the data are compressed into the latent space \(h_{n}=k\). The decoder then reconstructs the original data dimensions based on the latent space data through reverse mapping. The goal of the autoencoder is to minimize the reconstruction error between the input data \(\mathbf{X}\) and the reconstructed data \(\mathbf{X}^{\prime}\).