A Tattered Cloak of Invisibility: Measuring Anonymity Loss in Railgun on Ethereum
From a user's perspective, perhaps the most significant difference between traditional banking services and widely used blockchain-based financial systems is that, in the latter, transactions and, either directly or indirectly, account balances and transaction histories are publicly observable. Therefore, a growing number of cryptographic solutions have been proposed to add a privacy layer to such systems. However, the privacy that users actually obtain does not depend solely on the security of the underlying cryptographic protocol: user behavior, transaction amount patterns, and timing decisions can substantially reduce anonymity. In this work, we study behavioral leakage in cryptocurrency mixers, focusing on Railgun on Ethereum. We aim to heuristically estimate the probability that a given deposit and withdrawal transaction belong to the same user. We consider five sources of leakage: characteristic timing patterns, address reuse, proximity in the transaction graph induced by prior public transactions, amount fingerprints that preserve distinctive digit patterns across transaction values, and knapsack type matches in which groups of transaction amounts add up in revealing ways. Our results show that even cryptographically strong privacy systems may suffer substantial anonymity loss due to user behavior and transaction patterns. Our five heuristics are able to uniquely link 17.65% of Railgun withdraw transactions to deposit transactions. We also applied a knapsack solver algorithm that was able to produce a 3.42 bit median anonymity loss for withdraw transactions. This work contributes to a better understanding of the practical privacy limits of mixers and anonymity pools, and points toward safer usage practices and design principles.