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Supply chain finance (SCF) provides credit for small and medium-sized enterprises with low credit lines and small financing scales. The resulting financial credit data and related business transaction data are highly confidential and private. However, traditional SCF management schemes use third-party platforms and centralized designs that cannot achieve highly reliable secure storage and fine-grained access control. To address such a need, we propose Fabric-SCF, designing and implementing a Blockchain-based secure storage system by utilizing distributed consensus to realize data security, traceability, and immutability. The attribute-based access control model is deployed for access control, also utilizing smart contracts to define system processes and access policies to ensure the system's efficient operation. To verify the performance of Fabric-SCF, two sets of simulation experiments are designed its effectiveness. Experimental results show that Fabric-SCF achieves dynamic and fine-grained access control while maintaining high throughput in a simulated real-world operating scenario. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a promising decentralized deep learning technology, which allows users to update models cooperatively without sharing their data. FL is reshaping existing industry paradigms for mathematical modeling and analysis, enabling an increasing number of industries to build privacy-preserving, secure distributed machine learning models. However, the inherent characteristics of FL have led to problems such as privacy protection, communication cost, systems heterogeneity, and unreliability model upload in actual operation. Interestingly, the integration with Blockchain technology provides an opportunity to further improve the FL security and performance, besides increasing its scope of applications. Therefore, we denote this integration of Blockchain and FL as the Blockchain-based federated learning (BCFL) framework. This paper introduces an in-depth survey of BCFL and discusses the insights of such a new paradigm. In particular, we first briefly introduce the FL technology and discuss the challenges faced by such technology. Then, we summarize the Blockchain ecosystem. Next, we highlight the structural design and platform of BCFL. Furthermore, we present the attempts ins improving FL performance with Blockchain and several combined applications of incentive mechanisms in FL. Finally, we summarize the industrial application scenarios of BCFL. [ABSTRACT FROM AUTHOR]

Stablecoins have enabled the rapid growth of decentralized payments and the birth of the new generation of payment system with cryptocurrencies and Blockchain technology. However, while some studies have investigated the price-performance or role of Stablecoins, a comprehensive overview focused on the full context, stabilization mechanisms, and payment applicability of Stablecoins is still missing. This paper first summarizes the definition and the current state of Stablecoins and then presents the relative ecosystem. More precisely, after summarizing the system structure and stability mechanism, we discuss its applicability for payment. The overview proposed in this paper represents the most comprehensive summary of research on Stablecoin. We identified that the asset-backed Stablecoins is the most efficient and widely used Stablecoins. On the other hand, the cryptocurrency-backed Stablecoins are currently the most balanced applicable to the original concept of Stablecoins. Again, the algorithm-backed Stablecoins have the most potential for development, though they hesitated without real collateral or deposit reserves and are potentially prone to collapse. Finally, we conclude and present possible future trends for Stablecoins. • This article represents the first overview of Stablecoins in a comprehensive perspective that investigates extensive ecosystem, mechanism, applicability as payment method, related monetary policy, use case, and risk considerations in this field. • Study focuses on the structure, stabilization mechanisms, and applicability of Stablecoins as a payment system. [ABSTRACT FROM AUTHOR]

• A Blockchain-based secure storage and sharing scheme for ELRs in MOOCs learning systems is proposed. • Aimed to leverage Blockchain, the proposed scheme can support efficient conditional anonymity, secure storage, and share without requiring complex cryptographic computations. • The security analysis shows that the proposed scheme achieves legitimate security assurance, outperforming other similar works. Massive Open Online Courses (MOOCs) have become a paramount online learning approach for flexible learning methods and extensive learning courses. Different from the traditional learning method, MOOCs advocate completing the learning process through online devices. Electronic Learning Records (ELRs) are vital for learners as compelling evidence of the learning process, generally are stored in a cloud data center. However, with trustless third-party storage, the security and privacy of ELRs cannot be guaranteed. Due to such, we propose a Blockchain-based secure storage and sharing scheme for ELRs in MOOCs learning systems. Designed to take advantage of blockchain, the proposed solution can support efficient conditional anonymity, secure storage, and sharing without the need for sophisticated cryptographic calculations. The experimental results and the security analysis shows that the proposed scheme achieves legitimate security assurance and outperforms other similar works. [ABSTRACT FROM AUTHOR]

The development of vehicular ad hoc networks (VANETs) is facing great challenges. Due to the open environment in VANETs, the false information sent by malicious vehicles not only affects the fairness of information interaction but also seriously threatens the driving safety of normal vehicles. Therefore, the study of trust evaluation and management in VANETs has become hot topics in recent years. In this paper, we propose a trust management model of VANETs based on blockchain. In this model, a hidden markov model (HMM) based vehicle trust evaluation method that improve the accuracy on the detection of malicious behavior is proposed. Besides, a trust management method based on the alliance chain is designed, which greatly improves the efficiency of trust updating and querying on the premise of security. Simulation results show that the model is promising and feasible, effective in the aspects of trust evaluation and trust management. [Display omitted] • We analyze the issues associated to the problem stated, and put forward the system model, attack model, and assumptions of the VANETs. • We propose a vehicle trust evaluation algorithm based on the HMM model with higher accuracy and better application value in the current environment of extreme computing performance. • We propose a trust management method based on hyperledger fabric, which improves the overall efficiency of the trust management and ensures better security. [ABSTRACT FROM AUTHOR]