Profiling Bitcoin Addresses for Ownership Recognition

Abstract

Bitcoin is one of the most widely used cryptocurrencies. It offers decentralization, transparency, and Pseudonymity. However, this leads to money laundering, illegal activities, and financial scams. Malicious users also try to bypass transparency by using third party mixing services to conceal origins of users and due to the vast number of transactions it becomes difficult to detect the ownership of bitcoin wallets. To address these challenges, profiling ownership of the bitcoin addresses becomes necessary. The proposed research compares traditional clustering techniques with transaction pattern analysis to identify which wallet addresses belong to which entities. Gini Impurity measure is used to evaluate how accurately the clusters are developed to detect ownership. Uncovering the relationship between these addresses is necessary to understand the behavior of users. This helps in identifying suspicious activities in the bitcoin network by mapping relations between the wallet addresses. This could aid in Anti-money laundering as a tool for crypto-forensics.