Enhancing Data Privacy Protection and Feature Extraction in Secure Computing Using a Hash Tree and Skip Attention Mechanism  
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and summarized the latest developments in deep learning methods for predicting the stock market. They identified data scarcity as a major barrier to creating profitable Deep Reinforcement Learning (DRL)-based Quantitative Trading (QT) methods. In the financial sector, as online transactions and digital payments become more common, a large amount of sensitive personal and business financial information needs stringent protection; for large spatio-temporal data stored on the cloud, data owners lose physical control over the data and face risks of tampering and deletion. [PERSON] et al. (2012) designed a blockchain-based secure storage mechanism, BSMD (blockchain-based secure storage mechanism for big spatio-temporal data), which uses an on-chain and off-chain cooperative storage model to mitigate the problem of insufficient blockchain storage capacity. This mechanism not only enhances the storage security of large spatio-temporal data but also provides traceability. Subvector commitments are constructed from the CDH assumption and bilinear pairs, and then a specific scheme for secure large data spatio-temporal storage is built using subvector commitments to ensure data integrity and consistency of on-chain and off-chain data. Their proposed protocol has batch processing capabilities, allowing data validators to query and verify in batches and data owners to update data in storage servers in batches, thus reducing computational costs. To implement security architectures to detect cloud intrusions and protect customer data from hackers and attackers, [PERSON] et al. (2013) combined Stochastic Gradient Descent Long Short-Term Memory (SGD-LSTM) and Blow Fish encryption to detect and prevent unauthorized cloud access. The planning system involves three phases: user registration, intrusion detection, and intrusion defense. The SGD-LSTM classifier predicts cloud data access and prevents unauthorized cloud access. During the data access phase, cloud data access is managed by authenticating authorized users using the Blowfish encryption algorithm. Experimental results show that compared to algorithms frequently used in cloud computing, their algorithm provides a high level of protection and has made considerable improvements in security and execution speed. The healthcare sector also faces challenges in data security, and [PERSON] et al. (2014) proposed a new model with an appropriate hash key blockchain security system algorithm to achieve high-speed data transmission rates. The analysis results show that the proposed method provides lower millisecond values, and the entire process is validated in a hospital environment for patient module data management systems. However, appropriate security and safety systems can be enhanced by models with machine learning technologies that include data blocks computed in security functions. In response to the aforementioned issues, this paper proposes a deep learning security computing model based on a hash tree and skip attention mechanism. This model combines the structural advantages of the hash tree with the efficient computational capabilities of the skip attention mechanism, significantly enhancing data security while greatly improving computational efficiency. Our research focuses on effectively protecting data privacy during large-scale data processing while maintaining efficient feature extraction. The main contributions of this paper are reflected in several aspects: First, we introduce the combination of the hash tree and the skip attention mechanism to address the inefficiencies of traditional secure computing methods when handling large-scale data. The hash tree structure ensures data integrity and security, with each node storing the hash value of a data block, allowing for the rapid verification of any data changes.