The data generated in the Internet of Things (IoT) holds significant transactional and utilitarian value. However, traditional data trading models face numerous challenges, including privacy leaks of raw data and limitations in arbitration. This paper proposes a secure and verifiable privacy-preserving data trading scheme for IoT (SVDT), in which perturbed data is substituted for raw data to fundamentally mitigate the risk of privacy leakage. Secondly, we construct a dual-verification mechanism based on homomorphic encryption and merkle trees to simultaneously achieve verifiability and confidentiality during the trading process. Additionally, smart contract facilitate escrow and automated execution to ensure trading fairness. Finally, a collusion-resistant anonymous arbitration mechanism is designed utilizing ring signatures to sever the association between the arbitrator’s identity and the adjudication result, thereby safe-guarding the anonymity and independence of the arbitration while effectively resolving disputes. Theoretical analysis demonstrates that the scheme effectively achieves data security and trading fairness, with security analysis validating its privacy-preserving properties. Experimental results show a favorable balance between performance overhead and privacy utility. The findings indicate that SVDT scheme provides a practical solution for fair trading involving IoT data.

Federated Large Language Model (FedLLM) shows excellent potential in collaboratively training large language models (LLM) under the federated learning (FL) framework, which is benefiting from its privacy protection advantage. However, FedLLM faces the significant challenge of the non-IID problem. In the real world, there are often cross-source or even cross-domain language set data between IoT devices. To address the issue, we propose a new FedLLM framework FedALoRA via personalized and efficient parameter fine-tuning (PEFT). Specifically, the proposed scheme combines the personalized aggregation method and the LoRA method, which can adaptively aggregate the downloaded global model and local model to the local target on each client while ensuring low training costs. This adaptation initializes the local model before each iterative training, enabling clients to learn general knowledge while enhancing their understanding of their own domain knowledge. Extensive experiments and analysis on cross-domain non-IID settings and the financial datasets on Dirichlet non-IID settings demonstrate the effectiveness and superiority of FedALoRA.

Cloud-fog automation architecture has propelled the advancement of the Industrial Internet of Things (IIoT), significantly enhancing production efficiency and intelligence through extensive data collection and connectivity. Simultaneously, industrial cyber-physical system leverages this data to achieve intelligent control and optimization of production processes. As industrial production becomes increasingly specialized and complex, independent operations within a single domain are no longer sufficient to meet demands, making cross-domain collaborative production inevitable. Consequently, ensuring the security of cross-domain communication and data sharing has become a critical issue for IIoT under the cloud-fog automation architecture. Existing solutions encounter substantial management and computational burdens in cross-domain communication and data sharing, and they are vulnerable to privacy leakage risks. To address these challenges and enhance industrial production efficiency, this paper uses consortium blockchain to co-design a cross-domain authentication and data sharing scheme. The scheme ensures secure and private cross-domain communications with minimal computational, communication, and storage overhead. And, the proposed time-specific plaintext checkable encryption protocol can secure data during cross-domain sharing. Security and performance analyses show that the proposed scheme effectively reduces computational and communication resource demands while maintaining communication and data security.

Addressing the common issues in the cultivation of collaborative innovation abilities for software engineering talents, such as imperfections in the industry-education integration system, inefficiencies in the management mechanism, and poor connections between the education chain, talent chain, and industry chain, this article introduces the implementation measures for cultivating the collaborative innovation abilities of software engineering talents from the perspectives of organizational models, training models, practical systems, and safeguard mechanisms under the context of industry-education integration.

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient.

The growing prevalence of graphs representations in our society has led to a corresponding rise in the publishing of graphs by researchers and organizations. To protect the privacy, it is important to ensure that graphs including sensitive data are not disclosed. Since the weight of edges could be utilized to infer confidential information, the graph should be privately published to avoid ethical and legal issues. In this paper, we propose a novel method for privately publishing shortest paths while preserving the privacy of sensitive edge weights in graph. Specifically, we divide the edge weights into internal and external edges based on their edge betweenness centrality. Then, we give two different differentially private algorithms to perturb edge weights based on the distinction between internal and external edges, respectively. To reduce the error ratios between differentially private shortest paths and real shortest paths, we employ edge betweenness centrality to search for the shortest path, which is closest to the true one. Our experimental results show that our mechanisms can effectively reduce the error in the average shortest path distance by 1.1% for large graphs, while for the shortest path change rate, our mechanisms can reduce it by 8.3%.