2018
DOI: 10.24251/hicss.2018.443
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Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning

Abstract: Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an entity's real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for reducing the anonymity of the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidenti… Show more

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Cited by 124 publications
(61 citation statements)
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“…To deal with imbalanced data, sampling-based approach and cost-sensitive approach are considered simultaneously in [18]. To reduce anonymity of Bitcoin by predicting yet-unidentified addresses, [19] trained classifiers with synthetic minority over-sampling technique [20] on imbalanced data. [21] introduces the idea of motifs in directed hypergraphs, defining exchange patterns of addresses.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…To deal with imbalanced data, sampling-based approach and cost-sensitive approach are considered simultaneously in [18]. To reduce anonymity of Bitcoin by predicting yet-unidentified addresses, [19] trained classifiers with synthetic minority over-sampling technique [20] on imbalanced data. [21] introduces the idea of motifs in directed hypergraphs, defining exchange patterns of addresses.…”
Section: Related Workmentioning
confidence: 99%
“…We leverage the dataset collected by [5] in order to facilitate the comparison between our method and the previous work. As described in Table II, the dataset contains totally 26,313 addresses with labels and owners, which are derived from a simple heuristic, naming multi-input transactions [11], sharedsend clustering [12], co-spend clustering [19], or common spending [6]. The idea is that the addresses of inputs in a transaction belong to the same entity because spending bitcoins needs the signature of the owner's private key.…”
Section: A Collect Datamentioning
confidence: 99%
“…In [10,12], methods for attacking Bitcoin user anonymity are presented. Both methods use the whole blockchain to create supervised machine learning models and classify Bitcoin entities.…”
Section: Related Workmentioning
confidence: 99%
“…Prior studies have tried to classify entities according to classes representing specific entity behavior within the network [10][11][12]. These techniques usually consider the whole blockchain and thus classification is performed considering all network (macro) dynamics among users.…”
Section: Introductionmentioning
confidence: 99%
“…The blockchain technology has gained substantial popularity in recent years, primarily in financial field, due to the cryptocurrencies. For example, Bitcoin was first introduced in 2008 [7] and ever since has attracted the attention of the research community from diverse academic fields [8], [9], [10] and gained mainstream popularity due to its unique characteristics, such as the absence of centralised control, an assumed high degree of anonymity and distributed consensus over decentralised networks. Blockchain solutions could reduce data breach risks by utilising threshold encryption of data together using public key infrastructure, where cooperation of multiple parties is required to decrypt data and asymmetric cryptography is used to authenticate communication with system participants [11].…”
Section: Introductionmentioning
confidence: 99%