In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. In order to address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we devise attribute atomic matching smart contracts based on precise data property alignment, with the objective of achieving fine-grained matching of data attributes between trading parties. In the trading execution phase, lightweight cryptographic algorithms based on elliptic curve cryptography (ECC) and non-interactive zero-knowledge proofs are employed for the dual encryption of trading data and the generation of attribute proof contracts, thus ensuring the security and privacy of the data. The results of experiments conducted on the Ethereum platform in an industrial IoT scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection.

Vehicle trajectory prediction plays a crucial role in IoT-based intelligent transportation systems, which can effectively address key issues such as driving safety and multi-vehicle collaboration. However, the sensitivity of trajectory data and the reluctance of data holders to share it constrain the prediction model’s ability to capture vehicle behavior patterns in different scenarios. To address the above problems, we propose a blockchain-enabled asynchronous federated proximal policy optimization framework (BE-AFPPO) for the trajectory prediction of self-driving vehicles. Firstly, we propose a Curiosity Proximal Policy Optimisation (C-PPO) algorithm. The method utilizes a driven exploration strategy to actively motivate the intelligent agent to explore the unknown state space. The avoidance policy model reaches a local optimum when processing trajectory data. In addition, we design historical GRU and future GRU as input layers. The target’s historical motion features and future trajectory features are extracted, respectively. Then, various data is received through asynchronous federated learning. This model can fully learn the vehicle’s behavior patterns in different scenarios, which improves prediction accuracy. Based on this, we develop a blockchain-based Dynamic Group Practical Byzantine Fault Tolerance (DG-PBFT) consensus algorithm. This enhances the credibility and integrity of the data while enriching the sources of trajectory data. Finally, we perform the experiments on the publicly available dataset nuScenes. The results demonstrate that the proposed method improves the robustness and accuracy of trajectory prediction.

Blockchain technology presents significant security challenges despite its transformative impact on digital transactions and decentralized data management. Key vulnerabilities include insecure smart contract execution, data privacy risks on transparent ledgers, and susceptibility of certain consensus mechanisms to attacks. Trusted Execution Environments (TEEs) offer a robust hardware-based solution to these critical issues. By providing isolated execution spaces, TEEs safeguard code and data confidentiality and integrity, thereby fundamentally strengthening blockchain security. This paper presents a comprehensive analysis of TEEs in blockchain technology. First, we analyze the challenges inherent in blockchain systems and demonstrate the advantages of TEEs over current methods. A detailed analysis of TEE properties, variants, and evolution in the blockchain field is provided. Additionally, we explore innovative TEE-based solutions across three key application domains: consensus mechanism optimization, confidential computation and execution, and payment networks and financial applications. Furthermore, we propose a research agenda addressing current challenges such as vulnerabilities to side-channel attacks and dependencies on hardware trust assumptions. Finally, we propose five critical directions for future TEE-blockchain integration: enhancement of security and privacy protection with particular attention to the Trusted Computing Base (TCB) minimization, performance optimization through hardware architecture advancement, trust model refinement to reduce centralization, expansion of application scenarios through interdisciplinary collaboration, and development of cross-chain interoperability standards. Our work contributes to blockchain security knowledge and provides a roadmap for researchers and practitioners in this rapidly evolving field.

Large models (LMs) exhibit transformative power in 6G Mobile-Edge Intelligence (MEI) scenarios, which can effectively address key challenges regarding network complexity, resource optimization, intelligent decision-making, and user experience. In this article, we first introduce the basics of LM and explain the potential applications and existing solutions for integrating LMs into 6G MEI. Afterward, we conduct a case study oriented to LM-assisted 6G MEI scenarios, focusing on how to use LMs to predict the size of traffic. Specifically, we employ two attention modules named Dartboard Spatial Multi-head Self-Attention (DS-MSA) and Causal Temporal Multi-head Self-Attention (CT-MSA) to extract spatio-temporal features. A Large Language Model (LLM) named GPT-2 is utilized as the backbone model. To fit time-series data, the original token encoding layer is replaced by the convolutional encoding layer. Additionally, to handle multi-scale temporal information, we adopt an additional temporal encoding layer. During training, the majority of the pre-trained parameters are frozen, with only the input/output layers and the fully connected layer involved in parameter updates. The simulation results demonstrate that, compared to the traditional AI-based baselines, the proposed LM-based framework acquires a lower training loss and converges faster. Moreover, it achieves better prediction accuracy with less resource consumption.

The Internet of Things (IoT) and cloud technologies have significantly facilitated healthcare. In such a context, medical data are collected by the terminals from the patients, manipulated, and stored on the cloud by hospitals (doctors). This brings asymmetry problems in medical data access control, processing, and storage between doctors and patients, which results in medical data sharing face many challenges such as privacy leakage and malicious feedback from cloud servers on queries. To solve these asymmetry problems, this paper proposes a medical data sharing scheme with cloud-chain cooperation and policy fusion in the IoT. Regarding asymmetrical access control rights, a conflict resolution and fusion algorithm that enables co-authorization of medical data by the doctor and the patient is introduced. To balance the symmetry of medical data storage and processing, a cloud-chain cooperation ciphertext retrieval method is proposed by means of two-stage joint searching from cloud servers and the blockchain, which can not only detect malicious medical data feedback from cloud servers, but also improve the data search efficiency. The security analysis showed that this scheme satisfies the confidentiality and verifiability of the retrieved information, and the feasibility of the proposed scheme was demonstrated through experiments.