With rapid growth in data volume generated from different industrial devices in IoT, the protection for sensitive and private data in data sharing has become crucial. At present, federated learning for data security has arisen, and it can solve the security concerns on data sharing by model sharing on Internet of mutual distrust. However, the hackers still launch attack aiming at the security vulnerabilities (e.g., model extraction attack and model reverse attack) in federated learning. In this article, to address the above problems, we first design an application model of blockchain-enabled federated learning in Industrial Internet of Things (IIoT), and formulate our data protection aggregation scheme based on the above model. Then, we give the distributed K-means clustering based on differential privacy and homomorphic encryption, and the distributed random forest with differential privacy and the distributed AdaBoost with homomorphic encryption methods, which enable multiple data protection in data sharing and model sharing. Finally, we integrate the methods with blockchain and federated learning, and provide the complete security analysis. Extensive experimental results show that our aggregation scheme and working mechanism have the better performance in the selected indicators.

Achieving efficient and secure shared data in vehicular networks is important for the development of smart transportation. Sharing data among intelligent vehicles not only enriches vehicle services but also improves traffic safety and efficiency. However, due to the specific nature of vehicular networks, security and privacy concerns prevent data providers from participating in the data sharing process. In addition, the quality of the data shared in the vehicular network is uneven and unreliable, and the reliability and authenticity of data sharing need to be further improved. In this paper, we propose a novel consortium blockchain-based trust model in vehicular networks (COBATS) to achieve secure storage and data sharing. To improve the quality of data sharing, we also design a trust management model capable of filtering malicious recommendations, which reduces the hazard of malicious nodes and ensures high-quality data sharing among vehicles. Moreover, we present a consensus mechanism with joint Proof-of-Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) to reduce resource consumption and improve the algorithm’s efficiency. The simulation results show that COBATS can improve the security and quality of data sharing. Furthermore, our model also can effectively handle certain attacks.

The blockchain-enabled cloud manufacturing is an emerging service-oriented paradigm, and the scheduling and process optimization for blockchain-enabled cloud manufacturing (SPO-BCMfg) are crucial to achieving the service-oriented goal. The blockchain-enabled cloud manufacturing paradigm improves the collaboration capabilities and information security over the ordinary cloud manufacturing while incorporating distributed storage, consensus mechanism, and cloud-edge collaboration. The above characteristics make SPO-BCMfg a multiobjective scheduling optimization problem. This article establishes the multiobjective SPO-BCMfg model based on a dynamic selection evolutionary algorithm to address the problem. First, we carry out the architecture and the modeling of the blockchain cloud manufacturing system. Then, a novel dynamic selection evolutionary algorithm is proposed, which is used to schedule and optimize the model for the process. In the stage of evolution, the algorithm uses a diversity-based population partitioning technique that utilizes the dynamic distance to realize the selection of elite solutions. The method was experimented on the SPO-BCMfg problem facing five and eight objectives. The experimental results show that the algorithm has a strong processing capacity in terms of convergence and diversity compared with the other advanced evolutionary algorithms.

A vulnerability or error in a smart contract will lead to serious consequences including loss of assets and leakage of user privacy. Established smart contract vulnerability detection tools define vulnerabilities through symbolic execution, fuzz testing, and other methods requiring extremely specialized security knowledge. Even so, with the development of vulnerability exploitation techniques, vulnerability detection tools customized by experts cannot cope with the deformation of existing vulnerabilities or unknown vulnerabilities. The vulnerability detection based on machine learning developed in recent years studies vulnerabilities from different dimensions and designs corresponding models to achieve a high detection rate. However, these methods usually only focus on some features of smart contracts, or the model itself does not have universality. Experimental results on the publicly large-scale dataset SmartBugs-Wild demonstrate that this paper's method not only outperforms existing methods in several metrics, but also is scalable, general, and requires less domain knowledge, providing a new idea for the development of smart contract vulnerability detection.