Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users

Béres, Ferenc; Seres, István András; Benczúr, András A.; Quintyne-Collins, Mikerah

doi:10.1109/dapps52256.2021.00013

Cited by 29 publications

(14 citation statements)

References 19 publications

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“…Users can make mistakes, and there are various ways in which the depositor and recipient addresses may become linked, if a user does not interact with the protocol as intended. Béres et al (2020) and Wu et al (2022) have proposed various approaches to de-anonymize Tornado Cash transactions, making use of address re-use, transaction activity, transaction cost choice, or more complex transaction graph and network analyses. For a discussion on how to de-anonymize Tornado Cash transactions or an extended discussion on the anonymity set, refer to the above papers.…”

Section: Anonymity Setmentioning

confidence: 99%

Tornado Cash and Blockchain Privacy: A Primer for Economists and Policymakers

Nadler¹,

Schär²

2023

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It is difficult to retain privacy on a public blockchain. In contrast to popular belief, permissionless blockchains are completely transparent. All confirmed transactions are publicly observable and stored as part of the blockchain's history. The users' identities are only protected through the use of addresses that act as pseudonyms. This setup allows public blockchains to operate without any intermediaries and creates a system where everyone can mathematically verify the legitimacy and integrity of transactions as well as the current state of the ledger; but the setup raises severe privacy concerns.If someone obtains information that allows them to link a blockchain address to an entity, they may effectively observe that entity's entire transaction history and associated activity. Even if the entity uses multiple addresses, any link between these addresses may expose the fact that they belong to the same person. Moreover, the immutable and public nature of the data creates a setting where the data accrues over time and will always be available for analysis. The algorithms and tools to analyze the data will become more sophisticated, off-chain data more abundant, and computational constraints less relevant.

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Section: Anonymity Setmentioning

confidence: 99%

Tornado Cash and Blockchain Privacy: A Primer for Economists and Policymakers

Nadler¹,

Schär²

2023

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“…Other works, such as [21,22], also take inspiration from Trans2Vec. For the task of de-anonymization, which aims to identify two accounts belonging to a single user based on the proximity of account representations, Beres et al [2] evaluate 11 graph learning methods on ground-truth pairs collected from the ENS and Tornado coin-mixers. Among them, Diff2Vec [25] and Role2Vec [1] are considered the state-of-the-art methods.…”

Section: Related Work and Background 21 Ethereum Representation Learningmentioning

confidence: 99%

“…One application of de-anonymization is to trace the flow of money laundering. For example, Tornado Cash [2,27] provides coin-mixing services on Ethereum: a participant deposits certain amounts of ether into a Tornado mixer contract, and use another account to withdraw the deposited coins after a period of time. In our experiment, given a ground-truth pair of EOAs, we use the representation of the query EOA to query its top-𝑘 closest neighbors in the hidden space.…”

Section: De-anonymizationmentioning

confidence: 99%

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BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

Zhang

Bingqiao

et al. 2023

Proceedings of the ACM Web Conference 2023

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As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pretrained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features the superior modeling capability of Transformer to capture the dynamic sequential patterns inherent in Ethereum transactions, and addresses the challenges of pre-training a BERT model for Ethereum with three practical and effective strategies, namely repetitiveness reduction, skew alleviation and heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH outperforms state-of-the-art methods with significant enhancements in terms of the phishing account detection and de-anonymization tasks. The code for BERT4ETH is available at: https://github.com/git-disl/BERT4ETH.

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“…Both Optimistic and zk Rollups offer support importing existing EVM-bytecode with minor modifications, and thus, have a flexible support for smart contracts. Transaction deanonymization and user profiling are major privacy concerns that only zk Rollups address by default [182][183][184].…”

Section: G Congestion Attackmentioning

confidence: 99%

A Survey of Layer-Two Blockchain Protocols

Gangwal¹,

Gangavalli²,

Thirupathi³

2022

Preprint

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After the success of the Bitcoin blockchain, came several cryptocurrencies and blockchain solutions in the last decade. Nonetheless, Blockchain-based systems still suffer from low transaction rates and high transaction processing latencies, which hinder blockchains' scalability. An entire class of solutions, called Layer-1 scalability solutions, have attempted to incrementally improve such limitations by adding/modifying fundamental blockchain attributes. Recently, a completely different class of works, called Layer-2 protocols, have emerged to tackle the blockchain scalability issues using unconventional approaches. Layer-2 protocols improve transaction processing rates, periods, and fees by minimizing the use of underlying slow and costly blockchains. In fact, the main chain acts just as an instrument for trust establishment and dispute resolution among Layer-2 participants, where only a few transactions are dispatched to the main chain. Thus, Layer-2 blockchain protocols have the potential to transform the domain. However, rapid and discrete developments have resulted in diverse branches of Layer-2 protocols. In this work, we systematically create a broad taxonomy of such protocols and implementations. We discuss each Layer-2 protocol class in detail and also elucidate their respective approaches, salient features, requirements, etc. Moreover, we outline the issues related to these protocols along with a comparative discussion. Our thorough study will help further systematize the knowledge dispersed in the domain and help the readers to better understand the field of Layer-2 protocols.

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Blockchain is Watching You: Profiling and Deanonymizing Ethereum Users

Cited by 29 publications

References 19 publications

Tornado Cash and Blockchain Privacy: A Primer for Economists and Policymakers

Tornado Cash and Blockchain Privacy: A Primer for Economists and Policymakers

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

A Survey of Layer-Two Blockchain Protocols

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