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Blockchain-based applications are becoming more and more widespread in business operations. In view of the shortcomings of existing enterprise blockchain evaluation methods, this paper proposes a multi-source heterogeneous blockchain data quality evaluation model for enterprise business activities, so as to achieve efficient evaluation of business activity information consistency, credibility and value. This paper proposes a multi-source heterogeneous blockchain data quality assessment method for enterprise business activities, aiming at the problems that most of the data in enterprise business activities come from different data sources, information representation is inconsistent, information ambiguity between the same block chain is serious, and it is difficult to evaluate the consistency, credibility and value of information. The method firstly proposes an entity information representation method based on the Representation learning for fusing entity category information (CEKGRL) model, which introduces the triad structure of related entities in blockchain, then associates them with enterprise business activity categories, and carries out similarity calculation through contextual information to achieve blockchain information consistency assessment. After that, a trustworthiness characterization method is proposed based on information sources, information comments, and information contents, to obtain the trustworthiness assessment of the business. Finally, based on the information trustworthiness characterization, a value assessment method is introduced to assess the total value of business activity information in the blockchain, and a blockchain quality assessment model is constructed. The experimental results show that the proposed model has great advantages over existing methods in assessing inter-block consistency, intra-block activity information trustworthiness and the value of blockchain. [ABSTRACT FROM AUTHOR]

The consensus mechanism serves as the fundamental technology behind blockchain, enabling the establishment of a decentralized and reliable network system that achieves consensus among nodes without relying on the trustworthiness of individual nodes. In the enterprise composite blockchain, the master chain adopts a licensed public chain. However, existing consensus mechanisms cannot be directly applied to the licensed public chain. To address this, this paper proposes a dual-layer consensus algorithm that combines DPoS and PBFT. Aiming at the problem that DPoS voting mechanism commonly elects the node holding more tokens, which can lead to issues of political indifference. On the other hand, PBFT suffers from concerns such as inflexible and singular master node election, high communication complexity during the consensus process, and substantial overhead associated with view changes. In light of these challenges, this paper introduces an improved DPoS and BFT-based double-layer consensus algorithm designed specifically for the enterprise composite blockchain. Initially, the algorithm votes to generate a set of consensus nodes by improved DPoS verification pool selection. Subsequently, the elected consensus nodes generate blocks and conduct on-chain operations using a BFT consensus algorithm that integrates VRF and TS. Through experimental verification, this paper proposed algorithm effectively enhances system security and node activity without sacrificing usability. Moreover, comparative analysis against existing algorithms demonstrates higher transaction throughput, lower transaction latency, and reduced CPU load associated with this paper proposed algorithm. [ABSTRACT FROM AUTHOR]

In order to foster a modern economic system and facilitate high-quality economic development, it is crucial to establish a conducive business environment. Undoubtedly, the evaluation of the business environment for enterprises constitutes a prominent area of research. Nevertheless, ensuring the authenticity and security of the raw data sources provided by participating enterprises poses a challenge, thereby compromising the accuracy of the evaluation. To tackle this issue, an enterprise composite blockchain construction method for business environment is proposed in this paper, which stores the raw data of enterprises by the means of hybrid on-chain and off-chain. Initially, the enhanced hash function SHA256 is introduced to encrypt the raw data of enterprises. The encrypted data is subsequently stored in an off-chain Level DB database, which is based on non-volatile memory. This approach effectively alleviates the burden on communication and storage. Secondly, a composite storage strategy on-chain is adopted: the key values from the Level DB are stored in the DAG-based Conflux public blockchain, while the enterprise state data is stored in the consortium blockchain, so as to provide trusted evidence of business environment evaluation data. Finally, it is demonstrated through a large number of experimental comparisons that the enterprise composite blockchain construction method proposed in this paper exhibits better read and write performance, lower storage efficiency and storage overhead, and outperforms both the before-improved Level DB database and existing blockchain storage models. [ABSTRACT FROM AUTHOR]

Master–slave blockchain is a novel information processing technology that is domain-oriented and uses efficient cryptography principles for trustworthy communication and storage of big data. Existing indexing methods primarily target the creation of a single-structured blockchain, resulting in extensive time and memory requirements. As the scale of domain data continues to grow exponentially, master–slave blockchain systems face increasingly severe challenges with regards to low query efficiency and extended traceback times. To address these issues, this paper propose a multi-level index construction method for the master–slave blockchain (MLI). Firstly, MLI introduces a weight matrix and partitions the entire master–slave blockchain based on the master chain structure, the weight of each partition is assigned. Secondly, for the master blockchain in each partition, a master chain index construction method based on jump consistent hash (JHMI) is proposed, which takes the key value of the nodes and the number of index slots as input and outputs the master chain index. Finally, a bloom filter is introduced to improve the column-based selection function and build a secondary composite index on the subordinate blockchain corresponding to each master block. Experimental results on three constraint conditions and two types of datasets demonstrate that the proposed method reduce the index construction time by an average of 9.28%, improve the query efficiency by 12.07%, and reduce the memory overhead by 24.4%. [ABSTRACT FROM AUTHOR]

The existing blockchain system mostly adopts the equal mining mode. All bookkeepers (entities) record the ledgers on a single main chain, and the data storage is random. Moreover, in complex or classified financial scenarios, the data of the main chain is difficult to realize association or regular storage, resulting in low efficiency of storage and query. At the same time, in the existing blockchain system, event traceability is mostly only found in the source block, and the implicit association between entities cannot be identified, so the query has limitations. To solve these problems, this paper proposes a composite blockchain associated event tracing method. This method firstly constructs the blockchain composite chain storage structure model, proposes the concept of private chain and alliance chain, and realizes the adaptive data association storage in complex or classified scenarios. Secondly, on the basis of obtaining the event source entity block, the auxiliary storage space is established to transfer storage relevant data. A tracing method of associated entity block based on the Apriori algorithm is proposed, and then the obtained traceability entity block is constructed as the source event correlation graph, so as to describe the association relationship between the event entities. Finally, a risk assessment system based on reinforcement learning is proposed to realize the risk assessment of traceability entity. Experiments show that the composite blockchain associated event tracing method proposed in this paper can reduce 60% of the storage overhead, improve 90% of the query accuracy and 50% of the security. can reduce the storage overhead by 60%, improve the query accuracy by 90% and improve security by 50%. [ABSTRACT FROM AUTHOR]

In recent years, blockchain security issues have attracted widespread attention. Especially in terms of computing power security, when nodes have sufficient computing power, they can arbitrarily tamper with blockchain ledger data. As a huge collection of computing power, the blockchain mining pool is a potential malicious node. Multiple malicious nodes jointly conduct a double-spending attack on the blockchain, which brings great security risks to the blockchain. This paper studies the different combinations of double-spending attack by potential malicious mining pools, constructs a blacklist judgment strategy based on the attack characteristics, and proposes a blockchain-based multi-malicious double-spending attack blacklist management model. Firstly, an inner loop malicious game is proposed to analyze the competition trend between a single malicious node and other malicious nodes. Secondly, according to the attack mode of multiple mining pools, a joint attack model and a decentralized attack model are proposed, and the operation rules of the two attack models are mathematically derived. Then a blacklist management model is proposed, and after the blacklist is constructed, a blacklist judgment strategy is formulated. The blacklist uses decentralize mining pool judgments and joint mining pool judgments to extract malicious mining pool nodes, which stops further malicious behavior. And finally, simulation experiments show that the blacklist management model can significantly reduce the success of attacks and resist double-spending attacks effectively. [ABSTRACT FROM AUTHOR]

The blockchain runs in a complex topological network which is affected by the principle of consensus, and data storage between nodes needs to maintain global consistency in the entire network, which causes the data storage inefficient. At the same time, the information exchange between large-scale communication node groups leads to the problems of bandwidth expropriation and excessive network load. In response to these problems, this article proposes a second-degree branch structure blockchain expansion model. First, a ternary storage structure is established. Data use the way of fully integrated storage, multi-cell storage, and fully split storage, and data are classified and stored in parallel between the structures. Second, a second-degree branch chain model is constructed. The main chain forks into multiple sub-chains, and a free competition chain structure and a Z-type chain structure are defined; a two-way rotation mechanism is introduced to realize the integration and transition between chain structures. Finally, a set of malicious attacks is simulated to realize the security constraints of the blockchain, to verify the security of the second-degree branch chain model. Experiment shows that the second-degree branch structure expansion model proposed in this article has great advantages in data storage efficiency and network load. [ABSTRACT FROM AUTHOR]