# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 2 Related Work  
Currently, the methods for privacy-preserving graph embedding can be primarily categorized into two types: data perturbation methods and feature decoupling methods. Data perturbation-based methods, exemplified by k-anonymity [8] and its variants [9], employ data obfuscation strategies through graph modification and generalization to obscure sensitive individual information by reducing data granularity. However, k-anonymity strategies exhibit significant utility degradation as graph scale increases. Differential privacy (DP) [10], as a classical privacy preservation framework, guarantees mathematically rigorous privacy protection through randomized noise injection. For example, [PERSON] et al. [11] proposed adding Gaussian noise to graph neural network aggregation outputs, with privacy budget analysis demonstrating compliance with DP requirements. Nevertheless, these approaches fail to account for the heterogeneous importance of individual nodes within global graph structures, resulting in suboptimal personalized protection where critical nodes receive inadequate safeguards while trivial nodes suffer from over-protection. Furthermore, optimal privacy budget allocation for high-dimensional graph data remains computationally intractable [12], with numerical configurations predominantly relying on manual trial-and-error and iterative validation, substantially increasing computational overhead. Large-scale noise injection may also distort critical graph structural information, including node degree distributions, community structures, and subgraph patterns, thereby compromising data utility. To circumvent the dual challenges of utility degradation in data perturbation and computational complexity in noise scaling, feature decoupling-based methods have been proposed. These techniques aim to disentangle privacy-sensitive features from utility-preserving features during graph embedding, thereby preventing adversarial inference of sensitive node attributes through embedded representations. Motivated by this paradigm, several privacy-preserving graph embedding frameworks leveraging adversarial learning and fairness learning have been proposed. To begin with, [PERSON] et al. [13] eliminated the implicit association between node representations and sensitive attributes by constructing a composable fair constraint framework based on adversarial training. However, some researchers point out that it is very difficult to obtain all the private information during the training process in a real environment [14]. [PERSON] et al. [15] and [PERSON] et al. [16] propose a dual-objective framework using adversarial minimax gaming to jointly optimize task performance and privacy protection. A Key innovation lies in decoupling graph embedding into primary task learning and privacy preservation sub-objectives, achieved through iterative parameter optimization that directly obfuscates sensitive features. The method constructs adversarial mappings between feature spaces and privacy labels, systematically erasing sensitive information traces while maximizing embedding utility. [PERSON] et al. [17] proposed a graph privacy funnel framework based on the variational method, achieving the decoupling of sensitive attributes through the information bottleneck objective function and the GNN encoder. The work [18] studies the privacy leakage problem caused by network structure, proposes a new general homogeneity ratio to quantify privacy risk, develops a graph private attribute inference attack as an evaluation tool, and at the same time proposes a graph data publishing method combined with learnable graph sampling technology to effectively protect user privacy and achieve the best balance between privacy and utility. Furthermore, many researchers achieve privacy decoupling by exploring the relationship between privacy and non-privacy features. Especially, [PERSON] et al.