Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin

Paper: arXiv 2403.00523

Authors: Hugo Schnoering, Pierre Porthaux, Michalis Vazirgiannis

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.

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides a practical evaluation of Bitcoin entity clustering heuristics using block data and introduces a useful metric (‘clustering ratio’) alongside temporal analysis. While mathematically light, its empirical assessment of heuristics over time offers solid utility for blockchain analytics.

Research Flowchart

  flowchart TD
    A[Research Goal: Assess & Compare Bitcoin Address Clustering Heuristics] --> B[Data Input: Bitcoin Blockchain Transaction History]
    B --> C[Methodology: Apply 2 Existing & 4 Novel Heuristics]
    C --> D[Calculate Clustering Ratio: Measure Entity Reduction]
    D --> E[Temporal Evolution Analysis: Evaluate Performance Over Time]
    E --> F[Outcomes: Quantitative Justification & Effectiveness Trends for Heuristics]

Cited References (17)

1. Bitcoin: A peer-to-peer electronic cash system (2008)
2. Last Digit Tendency: Lucky Numbers and Psychological Rounding in Mobile Transactions (2023)
3. A fistful of bitcoins: characterizing payments among men with no names (2013)
4. Evolution of the Bitcoin address graph: An exploratory longitudinal study (2017)
5. Breaking bad: De-anonymising entity types on the bitcoin blockchain using supervised machine learning (2018)
6. An analysis of anonymity in the bitcoin system (2013)
7. Evaluating user privacy in bitcoin (2013)
8. Tracking bitcoin users activity using community detection on a network of weak signals (2018)

1 of 17 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.