Graph Neural Networks (GNNs) have been widely used in various domains, such as social networks and transportation networks. Previous research has shown that GNNs are vulnerable to inference attacks. Node Membership Inference Attacks (NMIA) on GNNs infer whether a set of graph data records belongs to the training graph data of a target model. Link Status Inference Attacks (LSIA) against GNNs aim to infer whether there exists a link between a pair of nodes in the graph used to train the target GNN model. Specifically, given black-box access to a GNN model, NMIAs and LSIAs are conducted by analyzing the outputs (e.g., confidence score vectors) from GNN models. The defense methods against these two score-based inference attacks face the challenges of achieving effective defense performance and maintaining the utility of GNN models. In this paper, we propose PriGraph, a defense mechanism to protect the node privacy and link privacy of training graph data, while maintaining the high accuracy of the target GNN models. PriGraph adds crafted adversarial perturbations to outputs of the target GNN model by deploying two key components, i.e., defense auxiliary classifier and adversarial perturbation generator, which are used to find the minimal adversarial perturbations that can reduce the attack accuracy while maintaining task performance of node classification. We evaluate PriGraph with different GNN models and multiple benchmark datasets. The results show that PriGraph can dramatically reduce the attack accuracy of NMIA and LSIA on GNNs, providing a superior trade-off between the model utility and privacy.

With the rapid advances in information and communication technologies, the Internet of Things (IoT) has become large and complex, bearing tremendous amounts of data and running devices in various scenarios. Leveraging artificial intelligence (AI) technologies, IoT can achieve superior information extraction, data analytics, and decision making, which has resulted in the revolutionized AI of Things (AIoT). AIoT can alleviate the pressure of storage, computation, and communication. Despite the promising features brought by combining AI technologies into IoT infrastructure, AIoT systems still face some serious challenges including inadequate efficiency, violation of security and privacy, lack of trust, and insufficient incentive. Blockchain featured by its distributed consensus and incentive mechanisms can be a promising technology for addressing the challenges in AIoT. AIoT employing blockchain is evolving with expectations of achieving efficient, secure, and trusted network activities. In this article, we first introduce the background of AIoT and blockchain. Then, we discuss the motivations for employing blockchain with its characteristics in AIoT. Furthermore, we comprehensively review existing solutions on blockchain for AIoT systems from the aspects of efficiency, security, privacy, trust, and incentive. Finally, we discuss the challenges and future research directions on blockchain for AIoT.