Research Of Reputation Consensus Based Model For Identifying Blockchain Illegal Data Uploaded To Blockchain

Blockchain combines a variety of technologies such as consensus algorithms,smart contracts and distributed storage,with features such as tamper-proofing and decentralization.Users of blockchain can utilize these features to store data on the blockchain,avoiding single point failures and data tampering issues associated with centralized databases,thus enhancing the security of data storage.However,malicious users can upload illegal data to the blockchain,resulting in serious security problem.Existing methods mainly identify data through blockchain nodes to prevent illegal data from being uploaded.However,existing methods for identifying illegal data being uploaded to the blockchain face the issues of low credibility,such as low credibility of the node evaluation mechanism,credibility issues with reputation consensus,and inability to encourage nodes to credibly identify data.Therefore,this article studies the reputation consensus based model for identifying blockchain illegal data uploaded to blockchain,and the main research contents are as follows:(1)For the problem that the existing blockchain-based node evaluation mechanism does not consider the credibility of inactive nodes,the paper proposes a blockchain based dynamic node evaluation mechanism to evaluate the credibility of nodes in the identification model.First,this paper proposes a direct credibility evaluation based on Beta distribution and an indirect credibility evaluation based on recommendation mechanism.Based on the Beta function and the recommendation mechanism,the direct credibility and indirect credibility evaluation module are constructed to evaluate the credibility of nodes in multiple dimensions and improve the credibility of the evaluation mechanism.Then,this paper proposes a credibility fusion mechanism based on information entropy.By using information entropy as a weight factor to fuse two kinds of credibility to comprehensively evaluate the credibility of the target node,it reduces the interference of human factors on credibility fusion and improves credibility of the node evaluation mechanism.Finally,a graded review mechanism is built for inactive nodes.Different levels have different review frequencies,and their credibility is re-evaluated according to the reputation value of the previous stage.The experiment results indicate that the dynamic node evaluation mechanism can credibly evaluate nodes’ credibility in the presence of malicious nodes.(2)For the credibility problems caused by the difficulty of selecting credible nodes and the unreasonable weights of nodes in the reputation consensus,the paper proposes a reputation model based consensus mechanism to reach the consensus of data illegality between identification nodes.Reputation consensus is composed of two parts: A reputation based random selection algorithm and a node weight mechanism.The reputation-based random selection algorithm will select the identification nodes according to the reputation value.The algorithm will prefer to select nodes with high reputation values to ensure the randomness and credibility of the identification nodes.The reputation-based node weight mechanism assigns weights to nodes according to the node reputation value and standard normal distribution.Then according to the proposed partition weight mechanism,different weights are assigned to nodes located in different intervals again,so as to avoid the problem of one vote for a single node due to high reputation value.The experimental results show that in the identification environment which the illegal data being uploaded to the blockchain,the proposed reputation consensus can more effectively select trusted nodes and the node weights have higher credibility.(3)For the existing identification model can’t motivate nodes to identify data credibly leads to the low credibility of the model,the paper proposes a reputation consensus based model for identifying blockchain illegal data uploaded to the blockchain to identify the data to be uploaded to the blockchain.First,the paper proposes an incentive mechanism based on game theory,analyzes the identification model that illegal data uploaded to the blockchain through game theory and Nash equilibrium principle,and motivates nodes according to their identification behavior.Nodes that fulfill their responsibilities correctly will be rewarded.In order to earn a higher income,identification nodes must correctly identify illegal data.Then the paper proposes a reputation consensus based model for identifying blockchain illegal data uploaded to the chain,which mainly includes a blockchain-based dynamic node evaluation module and a reputation model based consensus module.According to the structure of the two modules,the node state transition module and the identification state transition module are constructed,and conduct relevant experiments through smart contracts.The experimental results show that the incentive mechanism can encourage nodes to make credible identification and improve the credibility of the identification model.The model for identifying the illegal data being uploaded to the blockchain based on reputation consensus can effectively and credibly identify illegal data.In summary,for the problem of the credibility of the method for identifying blockchain illegal data uploaded to chain,the blockchain based dynamic node evaluation mechanism,the reputation model based consensus mechanism and the game-theory-based incentive mechanism start from different stages of data identification,and improve the credibility of the model for identifying illegal data uploaded to chain.The high credibility model for identifying the illegal data uploaded to the blockchain can credibly guarantee the security of the blockchain,further accelerating the development of blockchain technology and expanding the application scenarios of blockchain technology.