# A Blockchain-Based Trust Model for Uploading Illegal Data Identification
## 1 Introduction  
A blockchain [1] is essentially a decentralized distributed ledger, which is composed of a series of blocks that record data [2]. The block header of each block has the cryptographic hash value of the previous block and is connected to the previous block through this hash value [3]. The blockchain has the characteristics of decentralization [4], tamper-proof [5], and anonymity [6]. Due to the tamper resistance and anonymity of the blockchain, the blockchain attracts many malicious users who use these features to upload harmful information and other illegal behaviors [7]. The occurrence of these security incidents has greatly adversely affected the development of blockchain technology, so it is necessary to control the blockchain. One of the directions is to use the technology of the blockchain to control the uploading of illegal data. The first step is to identify the uploading of illegal data through the technology of the blockchain. How to use the technology of the blockchain to realize the reliable identification of uploading illegal data is an urgent problem to be solved. It is of great significance for the long-term development of blockchain technologyto strengthen the identification of illegal acts on the blockchain and establish an effective blockchain risk identification system. In recent years, many scholars have discovered illegal data information on the chain. In 2014, [PERSON] [8] proposed a modular framework called Bitodine for identifying blockchain, investigating CryptoLocker ransomware, and accurately quantifying the amount of ransom paid and victim information. In 2016, [PERSON] [9] identified and analyzed the data stored in the blockchain, classified various contents, and found some illegal information. In 2018, based on previous research, [PERSON] [10] identified blockchain fund transfers and uploaded illegal content and found that in more than 1600 documents, most of them were text or images, and these documents had obvious illegal content. In 2020, [PERSON] [11] identified and analyzed the blockchain hacker subnetwork and found that hackers would change BTC into certain specific funds. In addition, according to the network characteristics, hackers were classified into different hacker groups. From the above brief literature review, it is evident that blockchain can store illegal data, but neither of them proposes a credible method to reduce such illegal events. Therefore, it is urgent to propose a credible identification model to reduce the occurrence of uploading illegal data. In order to solve the problem of the low credibility of existing models, this paper proposes a blockchain-based trust model for uploading illegal data identification by adding a new identification blockchain to identify illegal data that is about to be on-chain in a decentralized voting manner. Nodes are designed as anonymous participants, which can be companies or individuals who wish to obtain income by identifying illegal data. Nodes are only selected as identification nodes by the selection algorithm. In the trust model, the incentive mechanism rewards or punishes the identification nodes based on their behavior. They must act honestly in order to obtain higher returns. The analysis of game theory and the Nash equilibrium principle proves its credibility. The main contributions of this paper are as follows:  
1. This paper proposes a trust model for uploading illegal data identification. The identification blockchain identifies the data by decentralized voting. Only when the number of votes exceeds the specified number will the identification blockchain consider the data as illegal data. The identification nodes can obtain benefits through the identification service. In order to obtain a higher income, they must identify the data fairly. 2. This paper designs a reputation-based random selection algorithm. The identification blockchain generates identification nodes by the reputation-based random selection algorithm. The algorithm tends to nodes with high reputation values.