Efficiently Answer Clustering Queries on Memory Constrained Devices by Summarizing and Caching Bitcoin Blockchain

Abstract

Bitcoin blockchain analysis helps law enforcement and financial institutions identify and stop bad actors who are using cryptocurrencies for illicit activity such as fraud, extortion, and money laundering. Blockchain address clustering is a major analysis technique in which all addresses appeared in blockchain is clustered so that each cluster contains addresses only from a single wallet. With a proper address tagging mechanism in place, clustering can be used to trace back transactions to its users. Bitcoin blockchain which keeps growing rapidly can be seen as the greatest challenge faced by blockchain analytics tasks, specifically address clustering. The execution time of existing address linking models are inversely proportional to available memory and exponentially increases with the growth of blockchain. But none of the available major Bitcoin analytics platforms hasn’t addressed this problem. In order to address this problem, we’ll be providing a solution which involves summarization and caching of Bitcoin blockchain to improve Bitcoin address clustering speed on memory constrained devices. Our address linking model outperformed all major Bitcoin analytics platforms including BlockSci, GraphSense and BitIodine in term of clustering speed and proved to perform efficiently on memory constrained devices as well.