Blockchain is distinguished by its decentralization and security, but it faces significant scalability challenges. Sharding is widely regarded as a key solution for improving blockchain scalability. However, existing sharding schemes, such as Monoxide, often suffer from load imbalance and excessive cross-shard transactions (TXs), which degrades system performance. To address these issues, we propose Sharding-aware NSGA-II (SNSGA-II), a variant of the Non-dominated Sorting Genetic Algorithm II, designed to optimize account allocation in blockchain sharding. We formulate the allocation problem as a multi-objective optimization task and develop a SNSGA-II-based sharding algorithm that dynamically adjusts account allocation to improve load balancing while minimizing cross-shard TXs. The proposed approach achieves a well-balanced trade-off in terms of Pareto efficiency, ensuring that improvements in load balancing do not come at the cost of excessive cross-shard TXs. We evaluate our method through simulation experiments on a real Ethereum dataset. The results demonstrate that our method significantly outperforms existing solutions, including Metis, Monoxide, and HyperChain, in key metrics such as throughput and TX confirmation delay.

In recent years, blockchain oracle, as the key link between blockchain and real-world data interaction, has greatly expanded the application scope of blockchain. In particular, the emergence of the Multi-Data Source (MDS) oracle has greatly improved the reliability of the oracle in the case of untrustworthy data sources. However, the current MDS oracle scheme requires nodes to obtain data redundantly from multiple data sources to guarantee data reliability, which greatly increases the resource overhead and response time of the system. Therefore, in this paper, we propose a Secure and Efficient Multi-data Source Oracle framework (SEMSO), where nodes only need to access one data source to ensure the reliability of final data. First, we design a new off-chain data aggregation protocol TBLS, to guarantee data source diversity and reliability at low cost. Second, according to the rational man assumption, the data source selection task of nodes is modeled and solved based on the Bayesian game under incomplete information to maximize the node's revenue while improving the success rate of TBLS aggregation and system response speed. Security analysis verifies the reliability of the proposed scheme, and experiments show that under the same environmental assumptions, SEMSO takes into account data diversity while reducing the response time by 23.5%.

In the industrial Internet of Things (IIoT) scenarios, data sharing can promote mutual collaboration among production parties to improve productivity and optimise resource allocation, but data sharing in industrial scenarios faces the risk of privacy leakage due to open networks. Attribute-based encryption (ABE) can be used to solve the problem of data sharing privacy leakage. However, there is still no effective privacy protection solution for data sharing and access control policies in blockchain, which may expose sensitive information of data owners. Moreover, existing schemes only focus on protecting data privacy and ignore the protection of users’ query privacy. In addition, most of these schemes are based on cloud servers, which may lead to data tampering and a single point of failure. To address these issues, we propose a blockchain-based data security sharing scheme (BAPIR) that combines attribute-based encryption and private information retrieval (PIR). In this paper, we implement fine-grained access control using ABE based on the inner product and protecting the access policy. Additionally, PIR technology is introduced to protect users’ query privacy. To achieve efficient blockchain data storage, IPFS is used for on-chain and off-chain collaboration, and complex computational tasks are outsourced to edge servers, ensuring secure and efficient data sharing. Finally, we demonstrate the security and efficiency of BAPIR through security analysis and performance evaluation.

Federated Learning (FL) is a distributed privacy-protecting machine learning paradigm that enables collaborative training among multiple parties without the need to share raw data. This mode of training renders FL particularly susceptible to attacks. A pivotal challenge within FL is the defense against Byzantine attacks, which can alter the model aggregation process and ultimately lead to the failure of Federated Learning convergence. Existing Byzantine defense mechanisms often result in diminished model accuracy in scenarios characterized by non-independent and identically distributed (non-IID) data. To alleviate this issue, we propose a novel defense approach named Fed-BRMC. This method combines a self-developed federated Dual-Model Contrastive Detection technique with Similarity Detection methods, leveraging historical update information from users to deeply mine the characteristics of model parameters, effectively distinguishing between Byzantine and honest users. Extensive experimentation has shown that Fed-BRMC significantly improves user identification accuracy in non-IID data scenarios compared to existing methods and notably enhances the global model accuracy of Byzantine-robust Federated Learning.