Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example

Hu LI; Yunfang CHEN; Wei ZHANG

doi:10.11959/j.issn.2096-109x.2023089

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Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example

Papers | 更新时间：2024-06-03

- Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example
- Chinese Journal of Network and Information Security Vol. 9, Issue 6, Pages: 140-153(2023)
- 作者机构：
  
  1. 南京邮电大学计算机学院，江苏南京 210023
  2. 南京邮电大学软件学院，江苏南京 210023
  3. 南京邮电大学网络空间安全学院，江苏南京 210023
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2023089
  CLC： TP309
- Online First：2023-12，
  
  Published：25 December 2023
- 稿件说明：
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Hu LI, Yunfang CHEN, Wei ZHANG. Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example[J]. Chinese Journal of Network and Information Security, 2023, 9(6): 140-153.
DOI：

Hu LI, Yunfang CHEN, Wei ZHANG. Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example[J]. Chinese Journal of Network and Information Security, 2023, 9(6): 140-153. DOI： 10.11959/j.issn.2096-109x.2023089.

摘要

混币技术为加强用户交易隐私而设计，但对“加密货币”监管使用的地址聚类规则造成严重干扰，因而常被黑客用作洗钱工具实现资金逃逸，这引发了金融监管部门对混币交易检测的关注。目前混币交易检测工作大多停留在单纯的数据分析与统计阶段，对混币交易的内部细节缺乏清晰的认知，并且没有可信的验证数据，因而检测方法的效果缺少可信度。CoinJoin是去中心化混币的代表性思想，Wasabi是基于此思想实现的商用方案中比较流行的一种。结合CoinJoin概念及其限制交易匿名集大小和混币金额的特点，提出了CoinJoin混币交易的通用检测方法：CoinJoin混币交易为多输入多输出交易，具有交易输出项个数大于输入中UTXO个数和输出项金额中存在大量重复值的特点。将CoinJoin的通用检测方法与相关研究中提到的Wasabi混币交易的部分特征相结合，得到了对于Wasabi的基础检测方法并完成检测。从Wasabi平台服务接口处获得可信验证数据集，对该数据集分析并完成两项工作：一是对 Wasabi 基础检测方法中的规则参数进行调准；二是提出交易输出项金额中重复值的最高频次与输入中 UTXO 个数比值的新指标，该指标可用来衡量用户参与混币金额的自由度。在这两项工作的基础上，得到用于Wasabi的改进检测方法。实验表明：Wasabi基础检测方法的召回率为94.2%，准确率为67.2%；经过可信验证数据集的分析反馈，改进检测方法的召回率达到 100%且准确率在 99%以上。根据通用检测方法对整个CoinJoin类型的混币交易的总市场规模进行预测评估，得到结论：当今市场中CoinJoin混币交易占所有比特币交易的数量上限为1.9‰，金额上限为3.7‰。

Abstract

Designed to enhance the privacy of user transactions

mixed coin technology has created disruptions to the address clustering rules typically used for cryptocurrency regulation.Consequently

hackers have begun utilizing mixed coin technology as a tool for money laundering and fund evasion

which has raised concerns among financial regulators regarding the detection of mixed coin transactions.Currently

most detection methods for mixed coin transactions rely on data analysis and statistics

lacking a comprehensive understanding of the internal workings of these transactions.This lack of knowledge may undermine the credibility and effectiveness of detection methods due to the absence of reliable verification data.CoinJoin

a decentralized mixed coin concept

represents one approach

and commercial implementations like Wasabi have gained popularity.Drawing from the characteristics of CoinJoin and its restriction on the size of anonymous transaction sets and mixed coin amounts

a general detection method for CoinJoin mixed coin transactions was devised.Such transactions typically involved multiple inputs and outputs

with more output items than UTXOs in the input

and a high occurrence of duplicate values among the output amounts.A basic detection method for Wasabi was developed by combining the generic detection method for CoinJoin with specific features of Wasabi

as identified in related studies

to complete the detection process.A trusted validation dataset was acquired from the Wasabi platform service interface

and this dataset was analyzed to achieve two objectives.First

the alignment of rule parameters in the Wasabi base detection method was accomplished.Second

a new metric was proposed

measuring the ratio of the highest frequency of duplicate values in the output amount of transactions to the number of UTXOs in the input.This metric assessed the level of user participation in mixed coin transactions

providing a measure of user freedom.Using these two approaches

significant progress is made in the detection of mixed coin transactions.The experiments show that the recall rate of Wasabi’s basic detection method is 94.2% and the accuracy rate is 67.2%.After the analytical feedback from the credible validation dataset

the recall rate of the improved detection method reaches 100% and the accuracy rate is above 99%.The total market size of the entire CoinJoin type of mixed coin transactions was evaluated and predicted based on a common test methodology.It is concluded that the number of CoinJoin mixed coin transactions in today’s mixed coin market represents 1.9 per 1 000 of all Bitcoin transactions and 3.7 per 1 000 of the transaction value at most.

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