# Research on Blockchain Transaction Privacy Protection Methods Based on Deep Learning
## 2 Preparatory Knowledge
### Graph Neural Networks  
GNNs (graph neural networks) [22] are a deep learning model for processing graph-structured data. They are capable of capturing complex relationships and dependencies between nodes in a graph. The representation of each node is updated based on information from neighboring nodes and propagated through the neural network layers. This enables GNNs to handle a wide variety of graphical architecture tasks. The important strength of GNNs is they can operate directly on the graph structure, effectively utilizing the topological information of the graph. They have been widely used for tasks such as social network analysis, recommender systems and knowledge graphs. Common GNN variants include GCNs (graph convolutional networks) [23], GATs (graph attention networks) [24], and GAEs (graph autoencoders) [25], which use different mechanisms to aggregate and update node information. GCNs process graph data based on the concept of convolution, and its core principle is to update the representation of each node by aggregating information from neighboring nodes, thus capture the topology of the graph. In GCNs, the new feature representation of each node is realized by weighted average aggregation of its own features and the features of neighboring nodes. This process can be considered as a special convolution operation. GATs are neural network models for graph-structured data; they incorporate an attention mechanism to aggregate information from neighboring nodes. The core feature of GATs are that different neighbors contribute differently to the central node, which is reflected by attention coefficients. Each node updates its feature representation by first calculating the attention coefficients of all its neighbors (including itself) and then aggregating the neighbors' features weighted by these coefficients, considering the relative positional relationships of the nodes and individual characteristics. GAEs are a type of autoencoder model specifically for graph data, combining the characteristics of autoencoders with the capabilities of graph neural networks to capture reduced-dimensional embeddings of graph nodes. The core idea of GAEs are to encode graph nodes into a compressed space using a graph neural network, and then use a decoder to restore the structural details of the graph, such as the interconnections between nodes. Blockchain transaction data can form complex graph structures, encompassing a variety of transaction entities and their interactions. This presents an ideal application scenario for GNNs. For instance, IBM's AI Lab has proposed using GNNs to identify money laundering rings in Bitcoin transactions [26]. Researchers created a temporal graph dataset comprising over 200,000 Bitcoin transactions (known as the Elliptic dataset) for identifying and classifying legal and illegal transactions. The graph in the dataset consists of 203,769 nodes and 234,355 edges, where nodes denote transaction entities and edges denote Bitcoin transaction flows between two entities. Each node is associated with 166 transaction-related features, including the first 94 local features representing the node's time step, in-degree and out-degree, payment expenditure fees, and derived features like the average amount of Bitcoin transactions; the remaining 72 are aggregated features obtained by aggregating maximum, minimum, standard deviation, and correlation coefficient information of neighbor transactions from the central node. About 2% of the nodes in the data are fraudulent, 21% are non-fraudulent, and the rest are unlabeled. Using GNN-based semi-supervised learning, each unlabeled Bitcoin transaction can be classified as illegal or legal. The purpose of anomaly identity authentication detection is to infer account identities by capturing characteristics of transaction patterns.