Improving Address Clustering in Bitcoin by Proposing Heuristics

The Bitcoin system uses anonymous transactions to protect users’ privacy, but attackers can use this defect of bitcoin transactions to discover the association between bitcoin addresses. At present, address clustering methods can make use of these vulnerabilities to associate the address as an entity to a certain extent. However, these address clustering methods have problems such as an insufficient inference rate of change addresses, inability to identify mixing transactions, and low efficiency of algorithm implementation. We propose some solutions to these problems. 1) We improve the method of change address identification to identify and mark more of them. 2) We propose a heuristic address clustering method related to mixing transactions, which can identify their privacy vulnerabilities. 3) We propose an incremental address clustering method that can store the historical state and more quickly discover the anonymity defect of Bitcoin. We use real Bitcoin transaction data to demonstrate our method’s feasibility and reliability.

I. Introduction

Bitcoin [1] is an open-source, decentralized currency trading system based on a P2P network. Members of the Bitcoin system jointly maintain mutual verification of the public ledger, and system nodes compete for bookkeeping rights of the public ledger through a large number of calculations. Since the books are public, the system protects users’ privacy through anonymous transactions. Users at both sides of a transaction use the address as a pseudonym. Other users can see the address, but cannot see a user’s real information. The transaction address is usually generated by the user through the wallet and is unrelated to the user’s identity, which is impossible to infer [2]. Since users can generate multiple wallet addresses according to their needs, it is not possible to analyze their trading habits directly through transaction addresses [3]. However, relationships between Bitcoin transactions expose user address relationships. Using these defects, address associations are analyzed by an address clustering algorithm. The many mixing transactions in the Bitcoin system confuse the relationships between funds and user addresses using a large number of Bitcoin transactions [4]. Users of mixing transactions can avoid exploitation by address clustering algorithms to a certain extent, but these services may have vulnerabilities.