# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 7 Conclusions  
In this paper, we propose a dynamic node privacy feature decoupling graph autoencoder that fundamentally addresses the static protection dilemma in graph privacy preservation. this study adopts the node-level self-attention weight matrix to dynamically identify highly privacy-sensitive nodes and further screen the key privacy nodes to construct the privacy information distribution of graph data. We propose to use HSIC to test the dependency relationship between privacy and non-privacy distributions. Through the method of alternating training, the importance score of each node can be comprehensively evaluated. Unlike conventional methods that apply uniform protection strategies, our method implements a selective shielding mechanism that automatically adjusts privacy preservation levels according to node privacy importance assessments. Experiments on two real-world datasets show that, compared with other privacy protection methods, our proposed method achieves node privacy protection while maintaining a high level of data utility, effectively balancing the inherent conflict between privacy and utility. In the future research, more potential privacy leakage factors can be considered, such as the high-order correlation between nodes, the interaction between communities or subgraphs, and global quantitative indicators, so as to establish a measurement model closer to the actual privacy threat.  
Conceptualization, Y.H. and J.T.; methodology, Y.H.; software, Y.H.; validation, Y.H.; formal analysis, Y.H.; investigation, Y.H.; resources, J.T.; data curation, Y.H.; writing--original draft preparation, [PERSON]; writing--review and editing, Y.H., J.T. and S.D.; visualization, [PERSON]; supervision, J.T.; project administration, J.T.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Guizhou Provincial Science and Technology Projects \"Research on Big Data Attribute Privacy Protection and Utility Trade-off Based on Graph Neural Networks\" (No. QianKeHe Basic MS [2025] 622) and \"Data Feature Privacy Protection Based on Generative AI\" (No. QianKeHe Basic--[2024] Youth 126).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Yale**} & \multicolumn{2}{c}{**Rochester**} \\ \cline{2-5}  & **AP** & **AUC** & **AP** & **AUC** \\ \hline VGAE & \(0.886\pm 0.003\) & \(0.891\pm 0.002\) & \(0.926\pm 0.001\) & \(0.924\pm 0.011\) \\ \hline APGE & \(0.784\pm 0.002\) & \(0.806\pm 0.002\) & \(0.892\pm 0.013\) & \(0.896\pm 0.002\) \\ PVGAE & \(0.817\pm 0.003\) & \(0.827\pm 0.014\) & \(0.909\pm 0.003\) & \(0.907\pm 0.002\) \\ GAE-MI & \(0.789\pm 0.010\) & \(0.811\pm 0.013\) & \(0.874\pm 0.002\) & \(0.879\pm 0.002\) \\ \hline ours & \(0.847\pm 0.001\) & \(0.859\pm 0.001\) & \(0.916\pm 0.002\) & \(0.912\pm 0.011\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance on link prediction.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The source code of the simulation program and the raw data are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.