Identifying Bitcoin users by transaction behavior

Abstract

Digital currencies, such as Bitcoin, oer convenience and security to criminals operating in the black marketplace.Some Bitcoin marketplaces, such as Silk Road, even claim anonymity.1 This claim contradicts the ndings in this work, where long term transactional behavior is used to identify and verify account holders. Transaction features, such as timestamp, coin-ow, and connectivity, contribute to revealing the account-holder's identity.The time between successive transactions is the result of low-frequency eects, such as the desire purchase an item and daily schedule, as well as higher frequency eects, such as hardware and network latency. In addition to transaction time-intervals, dynamic network features of each transaction, such as coin ow and number of edge outputs and inputs, can also be used to identify account-holders. In this paper, we propose novel methodology for identifying and verifying Bitcoin users based on the observation of Bitcoin transactions over time. The behavior we attempt to quantify occurs in the social band of Newell's time scale and is low-frequency compared to other behavioral biometrics. A subset of Blockchain 230686 is analyzed, selecting users that initiated between 100 and 1000 unique transactions per month for at least 6 dierent months. This dataset shows evidence of being nonrandom and nonlinear, thus a dynamical systems approach is taken. Identication and verication accuracies are obtained using monthly Bitcoin samples. Outgoing transactions, as well as both outgoing and incoming transactions, are considered. Results show an inherent lack of anonymity by exploiting patterns in long-term transactional behavior.

Blockchain for Cities—A Systematic Literature Review

“…Traffic forensics and frequency analysis can yield patterns that compromise the anonymity in PKI public key based identity mechanism [110], [244], [245]. Therefore, additional measures are taken to improve the anonymity.…”

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“…Several recent studies have investigated the relation among bitcoin addresses [2][3][4][5][6][7][8][9][10]. In these studies, the authors have adopted heuristic methods to analyze the relation among bitcoin addresses.…”

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“…In this section, we will describe heuristics based on transactions present in the public ledger. Some other methods exist, using IP-addresses collected in real time while transactions occur [2] or global activity patterns for very active users [12]. One can refer to [10] for a recent survey on such methods.…”