DMIA: A Disentangled-Based Method for Graph Convolutional Network Against Membership Inference Attack

As a well-known graph embedding method, Graph Convolutional Networks (GCNs) have been widely applied to recommendation systems and social media analysis, in which privacy concerns regarding sensitive data have emerged in the public view due to the collection of personal preferences. Although the regularization methods are introduced to improve the network's security, the GCN tends to memorize individual user information in latent representations susceptible to the Membership Inference Attack (MIA). In addition, the previous works focus on improving the security while hurting the utility, or vice versa, which induces “negative transfer”. In this paper, we propose a novel disentangled-based framework to defend MIA and alleviate the issue of negative transfer in multi-task learning. First, we divide the sensitive and practical channels from the latent representations of graph nodes to minimize their linear dependency. Then, to effectively train our model, we employ the sub-computational graphs to generate local gradients for different tasks and allocate losses to them. Finally, we propose a novel mixed updating strategy to accumulate the updating information of sub-computational graphs. Extensive experiments show that the proposed method can mitigate the risk of membership inference while ensuring model accuracy.