# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 4 Results and Discussion
### Limitations and Future Works  
This section aims to candidly assess the current shortcomings of the model and explore potential future research directions. Firstly, although the Merkle-Transformer has exhibited excellent performance in financial behavior detection and stock price prediction, its effectiveness in processing extremely large datasets has not been thoroughly tested. In practical applications, financial institutions may need to handle enormous volumes of data, posing higher demands on the model's processing capability and verification efficiency. Therefore, the model's performance and scalability in handling big data are critical areas for future focus. Secondly, despite the model's exemplary performance in security verification, new security threats may arise in practical deployment. For instance, adversarial attacks are an active research topic in the field of machine learning, and attackers might develop novel methods to evade the security mechanisms of the Merkle-Transformer. Consequently, continuous research and improvement of the model's security features to counter evolving threats are necessary. Furthermore, the performance of the Merkle-Transformer is also dependent on hardware platform configurations, as our experiments have shown. While the model maintains good performance across multiple hardware platforms, optimizing the model to reduce computational and storage overheads on resource-constrained devices, such as edge computing scenarios, is another key point for future research. This includes model compression, quantization, and other optimization processes. Future work should also include testing the generality of the model and verifying the potential application of the Merkle-Transformer in other types of financial tasks, such as credit scoring and market risk management. Additionally, exploring the model's performance in multi-task learning and cross-domain learning is a promising area, which would further enhance the model's practicality and flexibility. From a technical research perspective, the mathematical foundation and theoretical framework of the Merkle-Transformer also warrant further exploration. For instance, mathematical optimization of the combination of Merkle trees and Transformers, algorithmic improvements, and mathematical proofs of data integrity verification mechanisms are potential directions for in-depth future research. This would not only help improve the model's performance but also deepen our understanding of the model's working mechanism. Furthermore, implementing Merkle trees in various types of deep neural networks to ensure data integrity and security is a challenging yet highly promising task. Let us first consider the case of convolutional neural networks (CNNs) [31; 39]. In CNNs, we can compute the hash values of features at each layer, from convolutional layers to pooling layers and then to fully connected layers, to construct corresponding Merkle tree nodes. This helps verify the integrity of features at each layer and detects data tampering or loss when necessary. Another common deep neural network architecture is the residual network (ResNet) [40]. In ResNet, we can calculate hash values within each residual block and add them to the respective Merkle tree. This ensures the consistency of features within each block while maintaining the smooth flow of information throughout the network. Lastly, \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Experimental Setup** & **Communication** & **Latency** & **Computational Source** \\ \hline Baseline & 5.32 MB & 3.13 s & Mid \\ No Cache & 5.32 MB & 4.17 s & High \\ No Compression & 8.97 MB & 4.33 s & Mid \\ Low Partition & 21.78 MB & 5.81 s & High \\ Low Level & 15.69 MB & 5.64 s & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance impact of various Merkle-Transformer runtime configurations. for multi-layer perceptrons (MLPs) [41], we can compute hash values before each hidden layer and the output layer to build corresponding Merkle tree nodes.