# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 1 Introduction  
Graph Neural Network (GNN)-based graph embedding techniques have demonstrated powerful capabilities in processing and analyzing large-scale graph data, with applications in social network analysis [1], recommendation systems [2], and traffic flow prediction [3]. Graph autoencoders [4] further enhance the representational capacity of graph embeddings by integrating node representation learning and graph data reconstruction through an encoder-decoder framework. However, since graph autoencoders can highly fit both the topological structure and node attributes of graph data, they may inadvertently capture fine-grained details of the learned data, posing privacy leakage risks in the released graph embeddings. For instance, attackers could exploit the model's capabilities in multiple ways: they might reconstruct adjacency matrices through the decoder to expose sensitive social relationships, leverage the encoder's latent space to execute membership inference attacks [5], or perform attribute inference attacks by analyzing and quantifying specific data patterns present in the graph embeddings [6]. Therefore, privacy decoupling within the graph embedding process is a critical technique for preserving privacy.  
Moreover, prior work has not accounted for the varying global importance of nodes, which necessitates different degrees of privacy decoupling intensity. Quantifying node importance inherently involves weight information correlated with privacy features, and these weights directly impact the representational performance of nodes, influencing the analytical capability of graph embeddings in downstream tasks. Therefore, developing a privacy decoupling mechanism capable of dynamically calibrating protection intensity according to nodal importance levels emerges as a crucial yet understudied challenge in achieving trade-off between data privacy and utility.  
To solve the above problems, we designed a dual-channel privacy decoupling graph autoencoder based on the attention mechanism. Firstly, we proposed a node importance ranking algorithm. In the privacy graph autoencoder, the self-attention mechanism is used to quantify the importance of different nodes for privacy protection, and the nodes are ranked according to their importance. Secondly, sort by the importance of nodes and select the top-ranked nodes as the data distribution of privacy information. Then, in the utility graph autoencoder, Hilbert-Schmidt Independence Criterion (HSIC)[7] is used to measure the dependence between the corresponding nodes as a penalty term to optimize the network parameters of the utility graph autoencoder. Finally, the privacy rights of each node are dynamically evaluated and adjusted through alternating training, enabling the model to achieve privacy utility trade-ups even when dealing with complex data. The key contributions could be summarized as follows:  
* A novel node privacy ranking algorithm that employs attention mechanisms to dynamically score the privacy features of each node, quantifying their privacy importance. This method mitigates the influence of high-privacy nodes in the embedding while retaining low-privacy nodes to ensure data utility.
* We introduce the use of the Hilbert-Schmidt Independence Criterion (HSIC) to assess the dependency relationship between privacy and non-privacy distribution, which avoids the deviations that occur when using approximate methods by means of hypothesis testing
* A dual-channel privacy graph autoencoder that decouples embedded privacy and utility features of graph data. Freezing parameters during alternating training, prevents gradient interference during backpropagation, enhancing the stability of node privacy importance measurement.
* Comprehensive evaluation on real-world graph datasets using node classification and link prediction. Experimental results show that the proposed method effectively resists inference attacks on private information in node classification. It maintains high utility in link prediction while achieving an optimal privacy-utility trade-off.