Analyzing and de-anonymizing Bitcoin networks: An IP matching method with clustering and heuristics

The anonymity and de-anonymity of blockchain and Bitcoin have always been a hot topic in blockchain related research. Since Bitcoin was created by Nakamoto in 2009, it has, to some extent, deviated from its currency attribute as a trading medium but instead turned into an object for financial investment and operations. In this paper, the power-law distribution that the Bitcoin network obeys is given with mathematical proof, while traditional de-anonymous methods such as clustering fail to satisfy it. Therefore, considering the profit-oriented characteristics of Bitcoin traders in such occasion, we put forward a de-anonymous heuristic approach that recognizes and analyzes the behavioral patterns of financial High-Frequency Transactions(HFT), with realtime exchange rate of Bitcoin involved. With heuristic approach used for de-anonymity, algorithm that deals with the adjacency matrix and transition probability matrix are also put forward, which then makes it possible to apply clustering to the IP matching method. Basing on the heuristic approach and additional algorithm for clustering, finally we established the de-anonymous method that matches the activity information of the IP with the transaction records in blockchain. Experiments on IP matching method are applied to the actual data. It turns out that similar behavioral pattern between IP and transaction records are shown, which indicates the superiority of IP matching method.