Graph neural networks (GNNs) can retain the structural information of graph data when processing graph data via message passing mechanism. Recently, GNNs have been widely used in recommendation systems, social networks, finance, etc. However, GNNs are also vulnerable when encountering severe security and data privacy challenges. Membership inference attacks (MIA) on graph data can make the trained GNN model leak the training data, which causes serious privacy problems. However, all the studies reviewed so far suffer from the fact that there are few types of research on GNNs to defend against MIA. In this paper, we proposeMPGStack: a framework that utilizes ensemble learning of GNNs to safeguard against MIA. More concretely, we propose three ensemble strategies inMPGStack: KFold, GP-KFold, and LPA-KFold, which partition the graph at different degrees of granularity based on random uniform partition, class information, and label propagation, respectively.MPGStackcan enhance the target model's generalization performance, mitigate MIA, and boost classification accuracy. The experimental results conducted on seven benchmark datasets demonstrate thatMPGStackcan effectively defend against MIA and significantly reduce the attack model's success rate to the level of random guessing.MPGStackalso improves the classification accuracy of the target model up to 99% or more. Extensive experimental results demonstrate thatMPGStackis more effective, stable, and applicable to different datasets and scenarios than other methods, achieving random guessing attack models while improving the model's classification accuracy on different datasets.

Machine unlearning can fortify the privacy and security of machine learning applications. Unfortunately, the exact unlearning approaches are inefficient, and the approximate unlearning approaches are unsuitable for complicated CNNs. Moreover, the approximate approaches have serious security flaws because even unlearning completely different data points can produce the same contribution estimation as unlearning the target data points. To address the above problems, we try to define machine unlearning from the knowledge perspective, and we propose a knowledge-level machine unlearning method, namely ERM-KTP. Specifically, we propose an entanglement-reduced mask (ERM) structure to reduce the knowledge entanglement among classes during the training phase. When receiving the un-learning requests, we transfer the knowledge of the non-target data points from the original model to the unlearned model and meanwhile prohibit the knowledge of the target data points via our proposed knowledge transfer and prohibition (KTP) method. Finally, we will get the un-learned model as the result and delete the original model to accomplish the unlearning process. Especially, our proposed ERM-KTP is an interpretable unlearning method because the ERM structure and the crafted masks in KTP can explicitly explain the operation and the effect of un-learning data points. Extensive experiments demonstrate the effectiveness, efficiency, high fidelity, and scalability of the ERM-KTP unlearning method. Code is available at https://github.com/RUIYUN-ML/ERM-KTP