Could Network Information Facilitate Address Clustering in Bitcoin?

Neudecker, Till; Hartenstein, Hannes

doi:10.1007/978-3-319-70278-0_9

Cited by 56 publications

(30 citation statements)

References 11 publications

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“…Many techniques to break the anonymity of Bitcoin users have been proposed in the literature. This is achieved either by grouping together ("clustering") the addresses controlled by each user [3], [4], [5], or by using observations on the underlying peer-to-peer network [34], [35], or by combining both techniques [36].…”

Section: B Address Clusteringmentioning

confidence: 99%

Data Mining for Detecting Bitcoin Ponzi Schemes

Bartoletti

Pes

Serusi

2018

2018 Crypto Valley Conference on Blockchain Technology (CVCBT)

213

133

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Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find new investments. Despite being illegal in many countries, Ponzi schemes are now proliferating on Bitcoin, and they keep alluring new victims, who are plundered of millions of dollars. We apply data mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our starting point is a dataset of features of real-world Ponzi schemes, that we construct by analysing, on the Bitcoin blockchain, the transactions used to perform the scams. We use this dataset to experiment with various machine learning algorithms, and we assess their effectiveness through standard validation protocols and performance metrics. The best of the classifiers we have experimented can identify most of the Ponzi schemes in the dataset, with a low number of false positives.

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Section: B Address Clusteringmentioning

confidence: 99%

Data Mining for Detecting Bitcoin Ponzi Schemes

Bartoletti

Pes

Serusi

2018

2018 Crypto Valley Conference on Blockchain Technology (CVCBT)

213

133

View full text Add to dashboard Cite

show abstract

“…Network information from the P2P network has also been used, alongside with address clustering heuristics, to check whether such information could be useful in the deanonymization of Bitcoin users [14]. The study shows how while most of the network information cannot ease the address clustering process, a small number of users show correlations that may make them vulnerable to network based deanonymization attacks.…”

Section: Related Workmentioning

confidence: 99%

TxProbe: Discovering Bitcoin’s Network Topology Using Orphan Transactions

Delgado-Segura

Bakshi

Pérez-Solà

et al. 2019

Lecture Notes in Computer Science

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Bitcoin relies on a peer-to-peer overlay network to broadcast transactions and blocks. From the viewpoint of network measurement, we would like to observe this topology so we can characterize its performance, fairness and robustness. However, this is difficult because Bitcoin is deliberately designed to hide its topology from onlookers. Knowledge of the topology is not in itself a vulnerability, although it could conceivably help an attacker performing targeted eclipse attacks or to deanonymize transaction senders. In this paper we present TxProbe, a novel technique for reconstructing the Bitcoin network topology. TxProbe makes use of peculiarities in how Bitcoin processes out of order, or "orphaned" transactions. We conducted experiments on Bitcoin testnet that suggest our technique reconstructs topology with precision and recall surpassing 90%. We also used TxProbe to take a snapshot of the Bitcoin testnet in just a few hours. TxProbe may be useful for future measurement campaigns of Bitcoin or other cryptocurrency networks.

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“…All these studies are made possible by novel strategies to perform the blockchain representation and the clustering of Bitcoin addresses in a more efficient way [15][16][17][18]. In particular, Pinna et al [19] used a bipartite graph (represented as Petri net), to describe as nodes both entities and transactions and to allow performing investigations and statistics, and Bartoletti et al [20] proposed a general framework to deeply analyze blockchain data properly stored in a database, by using the database query language.…”

Section: Related Workmentioning

confidence: 99%

Applying the ETL Process to Blockchain Data. Prospect and Findings

et al. 2020

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We present a novel strategy, based on the Extract, Transform and Load (ETL) process, to collect data from a blockchain, elaborate and make it available for further analysis. The study aims to satisfy the need for increasingly efficient data extraction strategies and effective representation methods for blockchain data. For this reason, we conceived a system to make scalable the process of blockchain data extraction and clustering, and to provide a SQL database which preserves the distinction between transaction and addresses. The proposed system satisfies the need to cluster addresses in entities, and the need to store the extracted data in a conventional database, making possible the data analysis by querying the database. In general, ETL processes allow the automation of the operation of data selection, data collection and data conditioning from a data warehouse, and produce output data in the best format for subsequent processing or for business. We focus on the Bitcoin blockchain transactions, which we organized in a relational database to distinguish between the input section and the output section of each transaction. We describe the implementation of address clustering algorithms specific for the Bitcoin blockchain and the process to collect and transform data and to load them in the database. To balance the input data rate with the elaboration time, we manage blockchain data according to the lambda architecture. To evaluate our process, we first analyzed the performances in terms of scalability, and then we checked its usability by analyzing loaded data. Finally, we present the results of a toy analysis, which provides some findings about blockchain data, focusing on a comparison between the statistics of the last year of transactions, and previous results of historical blockchain data found in the literature. The ETL process we realized to analyze blockchain data is proven to be able to perform a reliable and scalable data acquisition process, whose result makes stored data available for further analysis and business.

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Could Network Information Facilitate Address Clustering in Bitcoin?

Cited by 56 publications

References 11 publications

Data Mining for Detecting Bitcoin Ponzi Schemes

Data Mining for Detecting Bitcoin Ponzi Schemes

TxProbe: Discovering Bitcoin’s Network Topology Using Orphan Transactions

Applying the ETL Process to Blockchain Data. Prospect and Findings

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