Graph neural networks (GNNs) have been developed to mine useful information from graph data of various applications, e.g., healthcare, fraud detection, and social recommendation. However, GNNs open up new attack surfaces for privacy attacks on graph data. In this paper, we propose Infiltrator, a privacy attack that is able to pry node-level private information based on black-box access to GNNs. Different from existing works that require prior information of the victim node, we explore the possibility of conducting the attack without any information of the victim node. Our idea is to infiltrate the graph with attacker-created nodes to befriend the victim node. More specifically, we design infiltration schemes that enable the adversary to infer the label, neighboring links, and sensitive attributes of a victim node. We evaluate Infiltrator with extensive experiments on three representative GNN models and six real-world datasets. The results demonstrate that Infiltrator can achieve an attack performance of more than 98% in all three attacks, outperforming baseline approaches. We further evaluate the defense resistance of Infiltrator against the graph homophily defender and the differentially private model.

The time and monetary costs of training sophisticated deep neural networks are exorbitant, which motivates resource-limited users to outsource the training process to the cloud. Concerning that an untrustworthy cloud service provider may inject backdoors to the returned model, the user can leverage state-of-the-art defense strategies to examine the model. In this paper, we aim to develop robust backdoor attacks (named RobNet) that can evade existing defense strategies from the standpoint of malicious cloud providers. The key rationale is to diversify the triggers and strengthen the model structure so that the backdoor is hard to be detected or removed. To attain this objective, we refine the trigger generation algorithm by selecting the neuron(s) with large weights and activations and then computing the triggers via gradient descent to maximize the value of the selected neuron(s). In stark contrast to existing works that fix the trigger location, we design a multi-location patching method to make the model less sensitive to mild displacement of triggers in real attacks. Furthermore, we extend the attack space by proposing multi-trigger backdoor attacks that can misclassify inputs with different triggers into the same or different target label(s). We evaluate the performance of RobNet on MNIST, GTSRB, and CIFAR-10 datasets, against four representative defense strategies Pruning, NeuralCleanse, Strip, and ABS. The comparison with two state-of-the-art baselines BadNets and Hidden Backdoors demonstrates that RobNet achieves higher attack success rate and is more resistant to potential defenses.