Resurrecting Address Clustering in Bitcoin

TL;DR

This paper improves Bitcoin address clustering by creating a ground truth dataset, developing new change address detection techniques, and analyzing the impact on blockchain analysis applications.

Contribution

It introduces a validated ground truth dataset and novel methods for more accurate change address detection and clustering in Bitcoin.

Findings

Enhanced clustering reduces false positives

Improved change address prediction accuracy

Better understanding of clustering impact on analysis applications

Abstract

Blockchain analysis is essential for understanding how cryptocurrencies like Bitcoin are used in practice, and address clustering is a cornerstone of blockchain analysis. However, current techniques rely on heuristics that have not been rigorously evaluated or optimized. In this paper, we tackle several challenges of change address identification and clustering. First, we build a ground truth set of transactions with known change from the Bitcoin blockchain that can be used to validate the efficacy of individual change address detection heuristics. Equipped with this data set, we develop new techniques to predict change outputs with low false positive rates. After applying our prediction model to the Bitcoin blockchain, we analyze the resulting clustering and develop ways to detect and prevent cluster collapse. Finally, we assess the impact our enhanced clustering has on two exemplary applications.