Change Address Detection in Bitcoin using Hierarchical Clustering
Bibliographic record
Abstract
The widespread adoption and success of blockchain, particularly Bitcoin, was influenced by the promise of decentralization and anonymity. Sadly, these same characteristics have made it attractive to illegal activities, requiring careful oversight and targeted interventions. In order to mitigate illicit usage of this technology, we need to analyze and de-anonymize transactions occurring in the blockchain. For that purpose, change addresses identification is a promising technique, since change addresses can be associated to the inputs of the same transaction since they are meant to hold leftover funds for the same user. In this article, we propose a new approach of change address detection using hierarchical clustering. First, we developed a new method for data extraction of connected transactions. After collecting the transaction, we combined multiple input heuristics with a hierarchical clustering algorithm at the transaction level to study similarities in usage patterns between inputs and outputs. After applying our detection model, we analyze the generating cluster and evaluate the performance of our solution in terms of F1-score, result accuracy, recall and precision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".