Information sharing has become an important application in modern supply chain management systems with business technology development. Because traditional supply chain information systems have problems such as easy data tampering, low information transparency, and interaction delays, blockchain has been taken consideration into supply chain information sharing research. Furthermore, blockchain technology is expected to provide decentralized supply chain information sharing solutions to enhance security, availability, and transparency. However, with the in-depth study of the application of blockchain technology in supply chain information sharing, people have found that the data stored publicly in the blockchain are still threatened by privacy leakage. In addition, due to the openness and accessibility of the blockchain, the lack of fine-grained access control is also apparent. In order to improve the security of data, we propose a novel privacy-preserving multiauthority attribute-based access control scheme for secure blockchain-based information sharing in a supply chain. In this scheme, blockchain stores encrypted supply chain information on distributed nodes. Multiple attribute authorities manage different attributes of users to achieve fine-grained access control and flexible authorization. Even if some attribute authorities fail, the user’s private key will not be leaked. In user secret key generation, we adopt an anonymous key generation protocol to realize the secure distribution of user keys by the attribute authorities. Furthermore, in order to meet the protection of communication privacy between blockchain nodes, properties of policy hiding and identity hiding are considered. Finally, we design experiments to analyze the performance of our scheme, including secret key sizes and running time of encryption and decryption.
Blockchain is gradually becoming an important data storage platform for Internet digital copyright confirmation, electronic deposit, and data sharing. Anomaly detection on the blockchain has received extensive attention as the foundation for securing blockchain-based digital applications. However, the current blockchain anomaly detection for obtaining network nodes' depth and dynamic change features still needs improvement. In this paper, we propose a blockchain anomaly detection method based on evolved graph attention. Different from general blockchain network modeling methods, we first adopt a dynamic attribute graph network construction method to model each transaction using edges to provide more learnable transaction attribute information for graph representation learning in blockchain networks. Then, we propose an evolved graph attention model structure, which learns the time evolution features of the blockchain network and dynamically updates the learning weights of the subnetwork nodes with different time steps while fully extracting the deep features of the blockchain nodes and avoiding the influence of noisy data on the classification results. In order to solve the dataset imbalance problem, we also apply the SMOTE method for graph-structured data on blockchain for the first time. Finally, we identify node labels in blockchain networks using a binary classification method and verify our proposed scheme through multiple rounds of experiments.
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