Identity Inference on Blockchain using Graph Neural Network

TL;DR

This paper introduces a novel end-to-end graph neural network model, I2BGNN, for blockchain identity inference, transforming transaction subgraphs into a classification task to improve accuracy and scalability.

Contribution

The paper presents a new graph neural network approach that effectively balances scalability and feature representation for blockchain identity inference.

Findings

Achieves state-of-the-art performance on EOSG and ETHG datasets.

Effectively transforms identity inference into a graph classification problem.

Reduces computational complexity compared to existing methods.

Abstract

The anonymity of blockchain has accelerated the growth of illegal activities and criminal behaviors on cryptocurrency platforms. Although decentralization is one of the typical characteristics of blockchain, we urgently call for effective regulation to detect these illegal behaviors to ensure the safety and stability of user transactions. Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. As a common tool, graph mining technique can effectively represent the interactive information between accounts and be used for identity inference. However, existing methods cannot balance scalability and end-to-end architecture, resulting high computational consumption and weak feature representation. In this paper, we present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in large-scale graph. Furthermore, we propose a generic end-to-end graph neural network model, named $\text{I}^2 \text{BGNN}$, which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity, achieving de-anonymization. Extensive experiments on EOSG and ETHG datasets demonstrate that the proposed method achieve the state-of-the-art performance in identity inference.