Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin

TL;DR

This paper evaluates the effectiveness of heuristic-based entity clustering methods in Bitcoin, introducing a new metric and analyzing their performance over time to improve blockchain analysis efficiency.

Contribution

It introduces the clustering ratio metric and assesses the temporal evolution of heuristics, including four novel ones, for better entity clustering in Bitcoin.

Findings

Clustering ratio effectively quantifies entity reduction.

Heuristics vary in effectiveness over time.

Novel heuristics show promising results.

Abstract

Exploring transactions within the Bitcoin blockchain entails examining the transfer of bitcoins among several hundred million entities. However, it is often impractical and resource-consuming to study such a vast number of entities. Consequently, entity clustering serves as an initial step in most analytical studies. This process often employs heuristics grounded in the practices and behaviors of these entities. In this research, we delve into the examination of two widely used heuristics, alongside the introduction of four novel ones. Our contribution includes the introduction of the \textit{clustering ratio}, a metric designed to quantify the reduction in the number of entities achieved by a given heuristic. The assessment of this reduction ratio plays an important role in justifying the selection of a specific heuristic for analytical purposes. Given the dynamic nature of the Bitcoin system, characterized by a continuous increase in the number of entities on the blockchain, and the evolving behaviors of these entities, we extend our study to explore the temporal evolution of the clustering ratio for each heuristic. This temporal analysis enhances our understanding of the effectiveness of these heuristics over time.