A Similarity Measure for Linking CoinJoin Output Spenders

This paper introduces a novel similarity measure to link transactions which spend outputs of CoinJoin transactions, CoinJoin Spending Transactions (CSTs), by analyzing their on-chain properties, addressing the challenge of preserving user privacy in blockchain systems. Despite the adoption of privacy-enhancing techniques like CoinJoin, users remain vulnerable to transaction linkage through shared output patterns. The proposed method leverages timestamp analysis of mixed outputs and employs a one-sided Chamfer distance to quantify similarities between CSTs, enabling the identification of transactions associated with the same user. The approach is evaluated across three major CoinJoin implementations (Dash, Whirlpool, and Wasabi 2.0) demonstrating its effectiveness in detecting linked CSTs. Additionally, the work improves transaction classification rules for Wasabi 2.0 by introducing criteria for uncommon denomination outputs, reducing false positives. Results show that multiple CSTs spending shared CoinJoin outputs are prevalent, highlighting the practical significance of the similarity measure. The findings underscore the ongoing privacy risks posed by transaction linkage, even within privacy-focused protocols. This work contributes to the understanding of CoinJoin’s limitations and offers insights for developing more robust privacy mechanisms in decentralized systems. To the authors knowledge this is the first work analyzing the linkage between CSTs.