Exploiting Transaction Accumulation and Double Spends for Topology Inference in Bitcoin
Bitcoin relies on a peer-to-peer network for communication between participants. Knowledge of the network topology is of scientific interest but can also facilitate attacks on the users’ anonymity and the system’s availability. We present two approaches for inferring the network topology and evaluate them in simulations and in real-world experiments in the Bitcoin testnet. The first approach exploits the accumulation of multiple transactions before their announcement to other peers. Despite the general feasibility of the approach, simulation and experimental results indicate a low inference quality. The second approach exploits the fact that double spending transactions are dropped by clients. Experimental results show that inferring the neighbors of a specific peer is possible with a precision of 71% and a recall of 87% at low cost.
This work was supported by the German Federal Ministry of Education and Research (BMBF) within the project KASTEL_IoE in the Competence Center for Applied Security Technology (KASTEL). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions.
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