Automatic Bitcoin Address Clustering

Ermilov, Dmitry; Panov, Maxim; Yanovich, Yury

doi:10.1109/icmla.2017.0-118

Cited by 103 publications

(67 citation statements)

References 12 publications

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“…A number of studies are focused on the problem of address clustering consisting of identifying the address set associated with a transacting entity, and typically rely on heuristics motivated by properties of the Bitcoin Blockchain protocol, such as the requirement of a unique signature for transaction inputs, see for instance [4], [14] and references therein for more details. The authors of [8] highlight that these methods crucially depend on address re-use behavior.…”

Section: B Related Workmentioning

confidence: 99%

Characterizing Entities in the Bitcoin Blockchain

Jourdan

Blandin

Wynter

et al. 2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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Bitcoin has created a new exchange paradigm within which financial transactions can be trusted without an intermediary. This premise of a free decentralized transactional network however requires, in its current implementation, unrestricted access to the ledger for peer-based transaction verification. A number of studies have shown that, in this pseudonymous context, identities can be leaked based on transaction features or offnetwork information. In this work, we analyze the information revealed by the pattern of transactions in the neighborhood of a given entity transaction. By definition, these features which pertain to an extended network are not directly controllable by the entity, but might enable leakage of information about transacting entities. We define a number of new features relevant to entity characterization on the Bitcoin Blockchain and study their efficacy in practice. We show that even a weak attacker with shallow data mining knowledge is able to leverage these features to characterize the entity properties.

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Section: B Related Workmentioning

confidence: 99%

Characterizing Entities in the Bitcoin Blockchain

Jourdan

Blandin

Wynter

et al. 2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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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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“…Blockchain users employ changeable PKs as their identity to protect their transactions from being linked together, i.e., classified, that eventually may lead to user deanonymization. Studies show user deanonymization is still possible using blockchain and off-the-chain information [11], i.e., other publicly available information in the Internet. While existing studies on blockchain transaction classification focus on Bitcoin, their findings are equally relevant to other cryptocurrencies that commonly record the sequence of coin exchange.…”

Section: Related Workmentioning

confidence: 99%

“…The authors in [11] cluster blockchain addresses based on not only the blockchain transactions, but also the available offthe-chain data, e.g., the instances where the PK is mentioned along with a tag that can be a company name. The numerical results show that the proposed method can successfully classify the transactions with higher rate compared to methods that are only based on the blockchain information.…”

Section: Related Workmentioning

confidence: 99%

On the Activity Privacy of Blockchain for IoT

Dorri

Roulin

Jurdak³

et al. 2019

2019 IEEE 44th Conference on Local Computer Networks (LCN)

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Security is one of the fundamental challenges in the Internet of Things (IoT) due to the heterogeneity and resource constraints of the IoT devices. Device classification methods are employed to enhance the security of IoT by detecting unregistered devices or traffic patterns. In recent years, blockchain has received tremendous attention as a distributed trustless platform to enhance the security of IoT. Conventional device identification methods are not directly applicable in blockchain-based IoT as network layer packets are not stored in the blockchain. Moreover, the transactions are broadcast and thus have no destination IP address and contain a public key as the user identity, and are stored permanently in blockchain which can be read by any entity in the network. We show that device identification in blockchain introduces privacy risks as the malicious nodes can identify users' activity pattern by analyzing the temporal pattern of their transactions in the blockchain. We study the likelihood of classifying IoT devices by analyzing their information stored in the blockchain, which to the best of our knowledge, is the first work of its kind. We use a smart home as a representative IoT scenario. First, a blockchain is populated according to a real-world smart home traffic dataset. We then apply machine learning algorithms on the data stored in the blockchain to analyze the success rate of device classification, modeling both an informed and a blind attacker. Our results demonstrate success rates over 90% in classifying devices. We propose three timestamp obfuscation methods, namely combining multiple packets into a single transaction, merging ledgers of multiple devices, and randomly delaying transactions, to reduce the success rate in classifying devices. The proposed timestamp obfuscation methods can reduce the classification success rates to as low as 20%.

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Automatic Bitcoin Address Clustering

Cited by 103 publications

References 12 publications

Characterizing Entities in the Bitcoin Blockchain

Characterizing Entities in the Bitcoin Blockchain

Applying the ETL Process to Blockchain Data. Prospect and Findings

On the Activity Privacy of Blockchain for IoT

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