## 3 Materials and Methods
### Dataset Preprocessing
#### 3.2.3 Data Preprocessing  
The data preprocessing process includes data cleaning, data annotation, and data augmentation. Each step aims to improve data quality and consistency while aligning closely with privacy protection goals to ensure user privacy protection in all scenarios. Data cleaning is the first and most fundamental step in data preprocessing. At this stage, raw data are filtered and processed to remove noise and irrelevant information. For text data, the cleaning process includes removing HTML tags, special characters, duplicate content, and non-text information. Additionally, text data need to be tokenized for further analysis and annotation. For image data, data cleaning mainly involves basic image processing operations such as cropping, rotation, and color adjustment to ensure image quality and consistency. For example, image normalization can be performed by adjusting the distribution of pixel values to conform to a standard normal distribution:  
\[\text{Normalized\_Image}=\frac{Image-\mu}{\sigma} \tag{8}\]  
where _Image_ represents the original image data, \(\mu\) and \(\sigma\) represent the mean and standard deviation of the image pixel values, respectively, and Normalized_Image represents the normalized image data.  
Next, data annotation is the core step in data preprocessing. At this stage, semi-automated annotation tools combined with manual verification are used to ensure high-quality and consistent data annotations. Finally, data augmentation is a critical step in data preprocessing. To improve the model's generalization ability and robustness, various augmentation techniques are applied to the data. For image data, techniques such as random cropping, rotation, scaling, and color transformation are employed. These augmentation techniques not only increase data diversity but also simulate different shooting conditions and scenes, enhancing the model's adaptability. For example, the mathematical expression for image data augmentation can be represented as  
\[\text{Augmented\_Image}=\text{Augment}(\text{Normalized\_Image}) \tag{9}\]  
where Augment represents the data augmentation function, and Augmented_Image represents the augmented image data. For text data, techniques such as synonym replacement, random deletion, and random insertion are used for data augmentation. These methods generate diverse training data without altering the text's semantics.  
Throughout the data preprocessing process, privacy protection is a core objective. By combining differential privacy techniques and user attention mechanisms, user privacy can be protected while improving data quality and consistency. This approach not only lays a solid foundation for subsequent data annotation and model training but also provides robust support for comprehensive privacy protection in all scenarios.  
The research is not limited to a specific field but is aimed at comprehensive data privacy protection across all scenarios. Therefore, whether in financial computing, medical imaging, or social media data analysis, the method can be effectively applied. By implementing systematic data preprocessing workflows, automated tools, and rigorous privacy protection strategies, both data quality and privacy protection are achieved, advancing the development of data privacy protection technology.