A Blockchain-Based Trust Model for Uploading Illegal Data Identification

Abstract

Malicious users can upload illegal data to the blockchain to spread it, resulting in serious threats due to the tamper-proof characteristics of the blockchain. However, the existing methods for uploading illegal data identification cannot select trust nodes and ensure the credibility of the identification results, leading to a decrease in the credibility of the methods. To solve the problem, this paper proposes a blockchain-based trust model for uploading illegal data identification. The trust model mainly has the following two core modules: Reputation-based random selection algorithm (RBRSA) and incentive mechanism. By assigning reputation attributes to nodes, the proposed RBRSA will select nodes according to reputation values. RBRSA favors the nodes with high reputation value to ensure the randomness and credibility of the identification nodes. The incentive mechanism is designed to ensure the credibility of the identification results through the credibility analysis of the model based on game theory and Nash equilibrium. Identification nodes that identify illegal data correctly will obtain incentives. In order to obtain a higher income, the identification nodes must identify illegal data correctly. Credibility analysis and comparative experiments show that the probability of selecting credible nodes by RBRSA is up to 23% higher than the random selection algorithm. The probability of selecting the nodes with a reputation value of 20 by RBRSA is 27% lower than the random selection algorithm; that is, the probability that RBRSA selects untrusted nodes is lower. Therefore, the nodes selected by RBRSA have superior credibility compared with other methods. In terms of the effect of the incentive mechanism, the incentive mechanism can encourage nodes to identify data credibly and improve the credibility of identification results. All in all, the trusted model has higher credibility than other methods.