Deanonymization and linkability of cryptocurrency transactions based on network analysis

peer reviewedBitcoin, introduced in 2008 and launched in 2009, is the first digital currency to solve the double spending problem without relying on a trusted third party. Bitcoin provides a way to transact without any trusted intermediary, but its privacy guarantees are questionable. Despite the fact that Bitcoin addresses are not linked to any identity, multiple deanonymization attacks have been proposed. Alternative cryptocurrencies such as Dash, Monero, and Zcash aim to provide stronger privacy by using sophisticated cryptographic techniques to obfuscate transaction data. Previous work in cryptocurrency privacy mostly focused on applying data mining algorithms to the transaction graph extracted from the blockchain. We focus on a less well researched vector for privacy attacks: network analysis. We argue that timings of transaction messages leak information about their origin, which can be exploited by a well connected adversarial node. For the first time, network level attacks on Bitcoin and the three major privacy-focused cryptocurrencies have been examined. We describe the message propagation mechanics and privacy guarantees in Bitcoin, Dash, Monero, and Zcash. We propose a novel technique for linking transactions based on transaction propagation analysis. We also unpack address advertisement messages (ADDR), which under certain assumptions may help in linking transaction clusters to IP addresses of nodes. We implement and evaluate our method, deanonymizing our own transactions in Bitcoin and Zcash with a high level of accuracy. We also show that our technique is applicable to Dash and Monero. We estimate the cost of a full-scale attack on the Bitcoin mainnet at hundreds of US dollars, feasible even for a low budget adversary