Sharding technology is crucial to achieve decentralization, scalability, and security simultaneously. However, existing sharding blockchain schemes suffer from high cross-shard transaction processing latency, low parallelism, incomplete cross-shard views of shard members, centralized reconfiguration, high overhead of randomness generation, and lack of formalized protocol design and security proofs. This paper proposes a flexible sharding (FS) blockchain protocol. First, a cross-shard Byzantine fault tolerance (CSBFT) protocol is designed to cut down confirmation delays when processing cross-shard transactions. Second, we utilize multiple parallel CSBFT where each node acts not only as a leader but also as multiple ordinary members to break through the performance bottleneck caused by a leader’s bandwidth and computing power, improving the system parallelism. Third, a cross-shard transaction censorship attack is proposed, and a cross-shard view-change mechanism is designed to defend against it. Fourth, a secure and truly decentralized shard reconfiguration method combining proof-of-work, proof-of-possession, and intra-shard BFT is designed. Fifth, we utilize a formal protocol design method and give strict security proof for each protocol. Finally, we evaluate FS from both theoretical and practical perspectives. FS is proven to have lower communication and computation complexity and achieve considerable performance.

To compensate for the lack of charging equipment, the charging reservation mechanism has been employed in the vehicle-to-grid (V2G) system, which is vulnerable to some potential risks, such as privacy disclosure, identity impersonation, hardware modification, and so on. In this paper, we first introduce a 5G-V2G system that integrates the 5G network and the power grid to enable the remote transmission of reservation information. On this basis, we propose a scalable reservation authentication and key agreement protocol that can support access of flexible number and guarantee the confidentiality and privacy of critical reservation data. The proposed protocol uses lightweight algorithms like reverse fuzzy extraction to offload the computation overhead on the EVs and embeds physical unclonable function (PUF) in the outfield equipment to resist physical attacks. We evaluate the security properties of the proposed protocol by the formal analysis tool AVISPA and compare it with the existing schemes in terms of computing overhead, transmission overhead, signaling overhead, and security. The comprehensive results indicate that our protocol performs better in all aspects and has higher reliability and scalability. In practice, our protocol can assist the 5G charging reservation mechanism to complete efficient, lightweight, and privacy-preserving reservation information collection and authentication.

Mobile edge computing (MEC) is to process, analyse, store and calculate the network data at the edge of the network. When the ground infrastructure is damaged in an emergency, the unmanned aerial vehicle (UAV) formation can be rapidly deployed to undertake the task of MEC. However, there are some potential problems to be considered in UAV‐enabled MEC, such as the trust among UAVs from different sources and the stability of UAV formation network. In view of the problems existing, this study proposes a blockchain‐based architecture to build a system of mutual trust, fairness, openness, and stability in this scenario. Through the implementation of blockchain technology, key data such as device computing capacity, task allocation, and task execution process are recorded openly, transparently, and irrevocably. As multi‐party trust is built to reduce the occurrence of fraud, system participants can get a reasonable reward. On this basis, the smart contract is used to ensure that algorithms are accessible to the public, and the sub‐blockchain technology improves the stability of the system. In the case study, the simulation results show that the resource consumption and time cost of the proposed scheme is reasonable and feasible.

Cross-silo federated learning (FL) is promising in facilitating data collaboration across various organizations, which greatly alleviates the information silo problem in industries and promotes the data intelligence of internet of things. With the advances of decentralized FL, the higher requirements of trust and privacy are put forward. Traditional FL heavily relies on a central coordinating server, which suffers from single points of failure and lacks trust in the correctness of aggregation results. What’s more, the intrinsic privacy issues of FL have aroused public attention, such as gradient inversion attack in local gradients. However, the privacy of quantized gradients remains serious and lacks attention, especially the most extremely 1-bit quantization in sign-based FL. In this paper, we demonstrate the potential privacy risk in sign-based FL by presenting a new gradient inversion attack, which successfully restores the original data from sign-based quantized gradients. And then we tackle the above two challenges via constructing a self-aggregation privacy-preserving federated learning atop blockchain, which takes advantage of a variant of ElGamal encryption to protect the privacy of local sign-based quantized gradients, and leverages the smart contract to achieve secure self-aggregation for participants without involving a centralized server. Moreover, we analyze that the proposed protocol achieves privacy and public verifiability. Finally, we evaluate the performance of the proposed protocol with a real deep learning model, and the results show that our protocol is resilient against gradient inversion attack in a decentralized environment without sacrificing learning accuracy.