## 3 Materials and Method
### Proposed Method  
#### 3.2.1 Overview  
In this study, we propose a deep learning model for secure computing that integrates hash trees and skip attention mechanisms, aiming to enhance the security and computational efficiency when handling sensitive data. The design of this model thoroughly considers the security needs during data storage and transmission, while incorporating deep learning technologies to boost the model's ability to recognize complex data patterns. The first part of the model involves the integration of a hash tree structure. The hash tree, also known as a Merkle tree, is a tree-shaped data structure where each node is linked to its children via hash values, primarily used to ensure the integrity and security of data. As demonstrated in Figure 4, the hash tree recursively combines the hash value of each data block with those of its adjacent blocks to form a new hash value, culminating in the root node's hash value. This process can be mathematically represented as follows:  
\[\mathrm{Hash}(N)=\mathrm{Hash}(\mathrm{Hash}(N_{1})+\mathrm{Hash}(N_{2})) \tag{2}\]  
where \(N\) represents a node, and \(N_{1}\) and \(N_{2}\) are child nodes. This structure ensures that any alteration in a data block will lead to a change in the root hash, thus quickly detecting any tampering with the data. The processed data first enters the skip attention mechanism module. Our model utilizes an improved attention mechanism--skip attention mechanism--that allows the model to skip unnecessary computational steps and directly focus on critical information through skip connections. Finally, to optimize the training process and enhance the model's sensitivity to key features, we designed the skip loss function. This loss function takes into account the important data features that the skip attention mechanism might overlook, emphasizing the learning of these features to ensure that the  
Figure 4: Hash-tree-based transformer model. Figure 3: Image dataset augmentation methods: (**A**) is CutOut; (**B**) is CutMix; (**C**) is Mosaic. model does not miss out on information crucial for security. The research presented in this paper indeed has broad practical application value, especially in fields where data security and privacy protection are critical, such as cloud-based medical diagnosis and finance. In medical diagnosis applications, patients' medical imaging data are often stored in the cloud to enable doctors or intelligent systems to perform diagnoses via remote access. However, cloud platforms face significant risks of data leakage, making it essential to effectively extract key information from images while ensuring patient privacy. The model proposed in this paper combines hash trees and the skip attention mechanism to encrypt image data, thereby protecting data privacy while ensuring the model's ability to extract critical features from encrypted data. During medical image analysis, the model can identify important features such as edges, textures, and contrasts, ensuring the accurate recognition of lesions like tumors and fractures, thus providing a secure and efficient solution for cloud-based medical diagnosis. In financial scenarios, particularly in the analysis of sensitive data such as stocks and futures, financial institutions typically store and process customer data in the cloud to support efficient data analysis and risk management. However, the privacy and security of financial data also face significant challenges, especially in cross-border data processing, where data leakage can lead to immeasurable economic losses. The method proposed in this paper encrypts the data before they are uploaded to the cloud, ensuring that even if unauthorized users access the encrypted data, they cannot retrieve specific customer information. In summary, the methods proposed in this paper have strong practical application potential in the fields of cloud-based medical diagnosis and financial data analysis.