Internet of Things (IoT) devices are widely considered in smart cities, intelligent medicine, and intelligent transportation, among other fields that facilitate people's lives, producing a large amount of private data. However, due to the mobility, limited performance, and distributed deployment of IoT, traditional access control methods cannot support the security of private data's access control process in current IoT environments. To address such problems, this article proposes an auditable access control model, based on an attribute-based access control model, and manages the access control policy for private data through the request record, the response record, and the access record stored in the blockchain network. Additionally, a Blockchain-based auditable access control system is also proposed based on the auditable access control model, ensuring private data security in IoT environments and realizing effective management and auditable access to these data. Experimental results show that the proposed system can maintain high throughput while ensuring private data security for real application scenarios in IoT environments.

Blockchain technology is rapidly changing the transaction behavior and efficiency of businesses in recent years. Data privacy and system reliability are critical issues that is highly required to be addressed in Blockchain environments. However, anomaly intrusion poses a significant threat to a Blockchain, and therefore, it is proposed in this article a collaborative clustering-characteristic-based data fusion approach for intrusion detection in a Blockchain-based system, where a mathematical model of data fusion is designed and an AI model is used to train and analyze data clusters in Blockchain networks. The abnormal characteristics in a Blockchain data set are identified, a weighted combination is carried out, and the weighted coefficients among several nodes are obtained after multiple rounds of mutual competition among clustering nodes. When the weighted coefficient and a similarity matching relationship follow a standard pattern, an abnormal intrusion behavior is accurately and collaboratively detected. Experimental results show that the proposed algorithm has high recognition accuracy and promising performance in the real-time detection of attacks in a Blockchain.

With the prevalence of Intelligent Transportation Systems (ITS), massive sensors are deployed on roadside, vehicles, and infrastructures. One key challenge is imputing several different types of missing entries in spatial-temporal traffic data to meet the high-quality demand of data science applied in Cooperative-ITS (C-ITS) since accurate data recovery is critical to many downstream tasks in ITSs, such as traffic monitoring and decision making. For such, it is proposed in this article solutions to three kinds of data recovery tasks in a unified model via spatial-temporal aware Graph Neural Networks (GNNs), named Spatial-Temporal Aware Data Recovery Network (STAR), enabling a real-time and inductive inference. A residual gated temporal convolution network is designed to permit the proposed model to learn the temporal pattern from long sequences with masks and an adaptive memory-based attention model for utilizing implicit spatial correlation. To further exploit the generalization power of GNNs, a sampling-based method is adopted to train the proposed model to be robust and inductive for online servicing. Extensive numerical experiments on two real-world spatial-temporal traffic datasets are performed, and results show that the proposed STAR model consistently outperforms other baselines at 1.5-2.5 times on all kinds of imputation tasks. Moreover, STAR can support recovery data for 2 to 5 hours, with its performance barely unchanged, and has comparable performance in transfer learning and time-series forecast. Experimental results demonstrate that STAR provides adequate performance and rich features for multiple data recovery tasks under the C-ITS scenario.

With the expansion of scale, the Internet of Things (IoT) suffers more and more security threats, and vulnerability and sensitivity to attacks are also increasing. As a distributed and secure network architecture, Blockchain is suitable for protecting the security and privacy of the IoT. In this article, we propose a secure smart blockchain IoT architecture based on Graph Neural Networks (GNN) named GTxChain, using a distributed intelligent prophecy machine to obtain off-chain data and construct the transaction data structure of the blockchain through the blockchain-directed acyclic graph (DAG). In the off-chain transaction and off-chain storage part, we use the lightning network, improved IPFS and GNN to obtain transaction information and continuously update the blockchain network and blockchain for transaction verification and other operations. GTxChain employs an IPFS storage architecture to enhance user privacy and reduce data processing time. Compared to other blockchain architectures, it improves by 10.51% and has better efficiency and stability in terms of Merkle-proof time overhead. Experimental results show that the GTxChain architecture can effectively ensure the IoT’s trustworthiness, security, and privacy (TSP).

Blockchain is a decentralized distributed ledger that combines multiple technologies, including chain data structures, P2P networks, consensus algorithms, cryptography, and smart contracts. This gives the blockchain the characteristics of decentralization, immutability, and traceability. However, blockchain stores smart contracts and transactions in blocks publicly, which poses the risk of data leakage and misuse. For example, by mining and analyzing blockchain transaction information, attackers can correlate transactions with user information, resulting in the disclosure of user privacy. Many current reviews focus on the privacy of permissionless blockchains or cryptocurrencies, requiring more in-depth investigations and detailed categorical analysis. To fill this gap, this work comprehensively reviews the latest and traditional methods related to identity, transaction, and smart contract privacy within permissioned and permissionless blockchains. Additionally, we summarize the existing problems, threats, and challenges of data management in different blockchain architectures. Last, we discuss future research directions for blockchain privacy protection technology, which can offer feasible ideas for researchers to explore further.