Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example
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...
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| Format: | Article |
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POSTS&TELECOM PRESS Co., LTD
2023-12-01
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| Series: | 网络与信息安全学报 |
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| Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023089 |
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| _version_ | 1841529647174516736 |
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| author | Hu LI Yunfang CHEN Wei ZHANG |
| author_facet | Hu LI Yunfang CHEN Wei ZHANG |
| author_sort | Hu LI |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cab8f2b952354af99ea129e6eb0b6e8b |
| institution | Kabale University |
| issn | 2096-109X |
| language | English |
| publishDate | 2023-12-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj-art-cab8f2b952354af99ea129e6eb0b6e8b2025-01-15T03:16:55ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2023-12-01914015359580850Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an exampleHu LIYunfang CHENWei ZHANGDesigned 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.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023089CoinJoinWasabimixed cointransaction detectionaddress clustering |
| spellingShingle | Hu LI Yunfang CHEN Wei ZHANG Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example 网络与信息安全学报 CoinJoin Wasabi mixed cointransaction detection address clustering |
| title | Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example |
| title_full | Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example |
| title_fullStr | Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example |
| title_full_unstemmed | Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example |
| title_short | Detection method of mixed coin transaction based on CoinJoin——take the Wasabi platform as an example |
| title_sort | detection method of mixed coin transaction based on coinjoin take the wasabi platform as an example |
| topic | CoinJoin Wasabi mixed cointransaction detection address clustering |
| url | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2023089 |
| work_keys_str_mv | AT huli detectionmethodofmixedcointransactionbasedoncoinjointakethewasabiplatformasanexample AT yunfangchen detectionmethodofmixedcointransactionbasedoncoinjointakethewasabiplatformasanexample AT weizhang detectionmethodofmixedcointransactionbasedoncoinjointakethewasabiplatformasanexample |