random beacons are crucial components in blockchain consensus, secure multiparty computation, and decentralized applications, providing high-quality randomness for these applications. However, random beacon services operated by a single organization face centralization issues and cannot be fully trusted by mission-critical applications due to possible breaches and collusion. Asynchronous distributed random beacon protocols are proposed as a promising alternative to such centralized services, since they can generate high-quality randomnesses that are unbiased and unpredictable for critical applications in the adversarial asynchronous Internet. However, they either suffer from expensive communication overhead or lack accommodation for efficient dynamic participation. To address these issues, we propose a practical asynchronous random beacon protocol that can be efficiently reconfigured to support rotations of participating nodes, reducing the reconfiguration’s communication complexity from O(λn3) to O(λ κn2), where λ is the cryptography security parameter, n is the size of nodes in the network, and κ is the small size of a any-trust sub-committee (which approximates a constant number about several dozens). We also demonstrate the performance and security of our scheme through thorough analysis and extensive experiments.

The rapid growth of decentralized finance (DeFi) has provided numerous benefits, but it has also presented significant economic security challenges. One of the most critical issues is Maximum Extractable Value (MEV). MEV refers to the opportunities for miners or validators to earn additional profits by altering the order of transactions. However, current MEV detection methods have notable limitations. These include poor adaptability of algorithms, the vastness of the search space, and the inefficiency of methods that rely on traditional heuristic approaches. To overcome these challenges, we introduces a reinforcement learning-based MEV optimization system for blockchain—RL-BES (Reinforcement Learning for Blockchain Economic Security). This system employs two deep reinforcement learning networks to optimize transaction ordering and template parameters, integrated with Monte Carlo Tree Search (MCTS) for effective path exploration. Furthermore, we presents a custom model evaluation tool designed to adjust various networks and parameters, facilitating the analysis of the best algorithmic solutions for on-chain MEV extraction. Experimental results indicate that the RL-BES system excels in multiple DeFi applications. It demonstrates faster convergence and consistently surpasses the performance of Flashbot and other similar detection tools.

With the development of artificial intelligence and big data, data has become an important part of production factors, and the sharing and transaction of data have a very high importance. Through the storage service of the cloud data transaction platform, users can send data to the cloud platform remotely, and flexibly access and transmit data through the Internet anytime and anywhere. However, this approach faces growing data security concerns. When users transmit data to the cloud, they will not have full control over their data. Data stored in the cloud may be altered, deleted, leaked, or misappropriated, especially in public cloud environments. Many current data transaction platforms simply adopt a decentralized model to avoid this problem, but still perform poorly in the face of massive transaction scenarios. In addition, when the data demander receives the required data, there is the problem of denying the transaction, which challenges the availability of the data transaction platform and affects the trust of the data transaction participants in the transaction platform. This paper proposes a secure searchable-encryption-based data transaction protocol (SDTP) utilizing blockchain technology and searchable encryption. In the proposed protocol, the transaction platform does not gain access to the provider’s raw data, and the data provider has all decisionmaking rights over the data. The data demander can search for the target encrypted data using only keywords before receiving the original data authorized by the data provider. In addition, blockchain technology, with its decentralized and tamper-proof characteristics, has made important contributions to the transformation of traditional centralized data transaction platforms, and the entire data transaction process is recorded on the blockchain, effectively preventing problems such as demander denial and data tampering. In this paper, a formal verification tool is used to ensure that the proposed protocol meets the ideal security standard expected by the secure data transaction protocol, and the security of the protocol against attacks is proved from the perspective of non-formal theoretical analysis.

Web3 is the next-generation internet, utilizing blockchain technology to power decentralized applications and give users greater control. However, the scalability limitations of blockchain create performance bottlenecks that hinder Web3’s overall processing capabilities. Among current scalability solutions, multi-chain architecture has been considered a promising approach with high flexibility. However, current multi-chain architecture lacks portability to existing blockchains and relies on relayers to solve timing issues in the interoperability process. The lack of portability makes it challenging for existing blockchains to adopt the current multi-chain architecture, significantly impeding multi-chain promotion. Moreover, relying on relayers to address timing issues leads to low efficiency and potential reliability risks. This paper introduces Zunesha, a multi-chain architecture that designs a smart-contractbased multi-chain toolkit (STACK) to provide a portable multi-chain architecture. Additionally, it introduces the Dynasty-Based Consensus Node Set Verification (DB-CNSV) protocol as a foundational safety mechanism to eliminate relayers in the interoperability process and address timing issues. Our evaluation shows that Zunesha significantly enhances the overall performance of the blockchain. As the number of subchains increases, the throughput grows almost linearly. Furthermore, the performance of inter-chain transactions surpasses that of the current mainstream multi-chain architecture, Cosmos.

This paper investigates the information sharing strategies of green supply chains, greenwashing, and blockchain technology. The unobservable green activities in the manufacturing process references to those aspects of logistics activities that are imperceptible to consumers. To ensure that consumers perceive authentic green information without being influenced by greenwashing, the retailer and manufacturer can collaborate on establishing a blockchain platform for sharing the manufacturer’s carbon footprint data. The research findings indicate that the manufacturer may be motivated to share its carbon footprint information to stimulate consumer demand for green products. Additionally, the retailer may proactively invest in constructing a blockchain platform to facilitate the sharing of the manufacturer’s carbon footprint data and enhance sales profitability. Analysis indicates that when consumers possess a higher anticipated level of unobservable greenness and exhibit greater sensitivity to green issues, there is an enhanced motivation for both manufacturers and retailers to implement blockchain technology. Interestingly, due to the construction costs associated with implementing the blockchain platform, the manufacturer and retailer are more likely to collaborate on this endeavor. However, this shift in the information sharing structure benefits all members of the supply chain, resulting in a mutually beneficial outcome. Furthermore, the dominant blockchain strategy is influenced by factors such as cost and market strategy.

Consensus mechanisms are fundamental to maintaining consistency in distributed systems. With the advent of Web 3.0, blockchain has revealed limitations of traditional Delegated Proof of Stake (DPoS) consensus mechanisms. To address these issues, we propose a novel Quadratic Voting-based DPoS (Q-DPoS) consensus mechanism. Our approach integrates Quadratic Voting into DPoS to optimize voting power distribution, vote counting, and reward settlement processes, thereby incentivizing participation from users with lower stakes while reducing the concentration of influence. To prevent the system from reverting to a linear reward structure under Sybil Attacks, we introduce admission rules and vote similarity detection mechanisms to strengthen its robustness. Simulation results demonstrate that Q-DPoS significantly increases voter participation and alleviates stake centralization, thereby enhancing overall decentralization. Additionally, theoretical analysis grounded in game theory confirms that the proposed mechanism effectively diversifies voting preferences, contributing to a more balanced and resilient consensus mechanism suitable for Web 3.0 ecosystem.