Introduction

This project is a collaborative work between: Hannah Wang, Karl Rohe, Yan Zhang

Bitcoin is a decentralized peer-to-peer currency network system. Every node in the network retains a copy of the blockchain, which records historical transactions and gets synced whenever a new transaction is added. Throughout the documentation, we will sometimes refer to "node" as "user" or "entity." In the network, any user can generate new transactions.

For each transaction, bitcoin encourages users to use new addresses to protect their anonymity. However, some users still reuse addresses. Previous studies have shown that it is possible to de-anonymize address control ships through address clustering heuristics and public address information. This pseudonymity feature of the bitcoin network allows researchers to study user transaction behaviors and detect abnormal criminal activities.

One group of users of particular interest for de-anonymization studies is the right-wing activists because many publicly advertise their addresses on the internet. Furthermore, right-wing entities can be websites or individuals; thus, they form a suitable scenario for discussing the feasibility of leveraging results from address clustering algorithms to study user transaction behavior. Built on this public information, we revisited one common heuristic for address clustering - transitive closure - and applied the algorithmically clustered entities to investigate the right-wing transaction pattern as a case study.