# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
###### Abstract  
In this paper, the Merkle-Transformer model is introduced as an innovative approach designed for financial data processing, which combines the data integrity verification mechanism of Merkle trees with the data processing capabilities of the Transformer model. A series of experiments on key tasks, such as financial behavior detection and stock price prediction, were conducted to validate the effectiveness of the model. The results demonstrate that the Merkle-Transformer significantly outperforms existing deep learning models (such as RoBERTa and BERT) across performance metrics, including precision, recall, accuracy, and F1 score. In particular, in the task of stock price prediction, the performance is notable, with nearly all evaluation metrics scoring above 0.9. Moreover, the performance of the model across various hardware platforms, as well as the security performance of the proposed method, were investigated. The Merkle-Transformer exhibits exceptional performance and robust data security even in resource-constrained environments across diverse hardware configurations. This research offers a new perspective, underscoring the importance of considering data security in financial data processing and confirming the superiority of integrating data verification mechanisms in deep learning models for handling financial data. The core contribution of this work is the first proposition and empirical demonstration of a financial data analysis model that fuses data integrity verification with efficient data processing, providing a novel solution for the fintech domain. It is believed that the widespread adoption and application of the Merkle-Transformer model will greatly advance innovation in the financial industry and lay a solid foundation for future research on secure financial data processing. f 2024 14 1386 10.3390/app14041386 Academic Editor [PERSON]  
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[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Integrating Merkle Trees with Transformer Networks for Secure Financial Computation  
the knowledge of identifying potential fraudulent activities and speculative operations to safeguard their investments. Lastly, the development of technological and data analysis tools can assist investors and analysts in making more informed decisions. By employing advanced algorithms and big data analysis techniques, they can gain a better understanding of market trends, identify potential risks, and consequently, formulate investment strategies with greater confidence. Traditional security measures, such as encryption and access control, provide protection to a certain extent, but their effectiveness is often limited in the face of complex cyber-attacks and internal threats [2]. Moreover, with the rapid development of financial technology, new security challenges continue to emerge, necessitating the development of more advanced and reliable security technologies. [PERSON] et al. [3] proposed an enhanced blockchain-based secure sharing (EBSS) scheme for real estate financial certificates that protects shared vouchers and allows for anonymous authentication; however, it is costly. [PERSON] et al. [4] designed a new method for secure online data backup to address issues such as data loss. [PERSON] et al. [5] researched a multi-chain model based on financial transactions and its corresponding cross-chain transaction protocol, achieving interoperability between homogeneous or heterogeneous blockchains, though the actual blockchain environment is complex, affecting its application effectiveness. In response, [PERSON] et al. [6] proposed an anti-tampering data query model based on B+ trees and Merkle trees, addressing the issue of single-query blockchain data systems, with experimental results indicating that this method performs best. Among the emerging technologies, Merkle trees (Merkle Tree) and the Transformer model have attracted widespread attention due to their unique characteristics.