A Graph Learning Based Approach for Identity Inference in DApp Platform Blockchain
Xiao Liu, Zaiyang Tang, Peng Li, Song Guo, Xuepeng Fan, Jinbo Zhang
Abstract
Current cryptocurrencies, such as Bitcoin and Ethereum, enable anonymity by using public keys to represent user accounts. On the other hand, inferring blockchain account types (i.e., miners, smart contracts or exchanges), which are also referred to as blockchain identities, is significant in many scenarios, such as risk assessment and trade regulation. Existing work on blockchain deanonymization mainly focuses on Bitcoin that supports simple transactions of cryptocurrencies. As the popularity of decentralized application (DApp) platform blockchains with Turing-complete smart contracts, represented by Ethereum, identity inference in blockchain faces new challenges because of user diversity and complexity of activities enabled by smart contracts. In this paper, we propose I <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> GL, an identify inference approach based on big graph analytics and learning to address these challenges. Specifically, I <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> GL constructs a transaction graph and aims to infer the identity of nodes using the graph learning technique based on Graph Convolutional Networks. Furthermore, a series of enhancement has been proposed by exploiting unique features of blockchain transaction graph. The experimental results on Ethereum transaction records show that I <inline-formula><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> GL significantly outperforms other state-of-the-art methods.