# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
###### Abstract
Merkle trees, a hash-based data structure, efficiently and securely verify data content and have been successfully applied in various distributed systems and cryptocurrencies. Their main advantage lies in the ability to verify data integrity, enabling the validation of a part of the data without revealing the entire content [7], which is crucial for protecting the privacy and security of financial data. On the other hand, the Transformer model, as a revolutionary deep learning architecture, has demonstrated outstanding performance in natural language processing (NLP) and several other domains [8]. Its main advantage is its capability to process sequential data, particularly in understanding complex, long-distance dependencies [9]. In the financial sector, this ability can be employed to capture complex patterns in transaction data, thereby improving the accuracy of financial fraud detection and the capability to predict market changes. [PERSON] et al. [10] proposed a financial product recommendation network based on the Transformer, showing that it has a faster training speed and better results than RNN. [PERSON] et al. [11] used the Transformer for stock trend prediction, proposing a Transformer encoder-based attention network (TeaNet) architecture, addressing the time-dependency issue of financial data. [PERSON] et al. [12] proposed an MTCformer method combining multi-channel TextCNN and Transformer for Ponzi scheme detection, utilizing the Transformer to extract semantic features, with experimental results showing that MTCformer achieved 97% accuracy, outperforming other methods (although it is time-consuming). [PERSON] et al. [13] proposed a Bitcoin trading model--Cryptocurrency BERT Incorporated Trading System (CBITS); research indicates that our model significantly outperforms other trading methods. However, despite the significant advantages of Merkle trees and the Transformer model, research combining the two in the field of financial security remains relatively limited. This paper proposes an innovative combination--the Merkle-Transformer model--to explore the application potential of these two technologies in secure financial computing. The objective is to enhance the security and efficiency of financial data processing by combining the data verification capability of Merkle trees with the data processing power of the Transformer model. [PERSON] et al. [14] proposed a method based on blockchain for sharing financial information between financial institutions, but it cannot guarantee the integrity of data during processing; [PERSON] et al. (2015) presented a quantitative analysis method for calculating the risk of cyber-attacks in the data security domain of finance; however, complex environments may affect the performance of this method in practical applications. [PERSON] et al. (2016) proposed a blockchain-based payment system for the Indian financial industry, which can achieve considerable speed, but it has not been tested in real-time, and the performance of the system under different network scenarios cannot be guaranteed. The design of the Merkle-Transformer model considers multiple aspects of financial data processing. First and foremost, the model ensures the security and integrity of data during processing, which is vital in domains such as financial transactions and stock price prediction. Secondly, by effectively integrating the structure of Merkle trees, the model provides an additional layer of security for financial data. This not only helps prevent data tampering but also makes the data verification process more efficient. Lastly, by combining the advantages of the Transformer model, our model exhibits excellent performance in processing large-scale financial data, especially in capturing complex patterns and long-term dependencies. In this paper, the design and implementation of the Merkle-Transformer model will be elaborated, including the verifiable computation module based on Merkle trees and the secure verifiable Transformer model.