Ethereum transaction graph analysis

Chan, Wren; Olmsted, Aspen

doi:10.23919/icitst.2017.8356459

Cited by 51 publications

(27 citation statements)

References 3 publications

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“…The most popular consensus algorithm in use today is proof of work (PoW), used in platforms such as Bitcoin and Ethereum [20]. In PoW, all agents are incentivised to participate in the consensus process, where agents leverage computational resources within their node to validate transactions for a monetary reward (often referred to as mining).…”

Section: Review Of Blockchain Technologymentioning

confidence: 99%

Blockchain based transactive energy systems for voltage regulation in active distribution networks

et al. 2020

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Section: Review Of Blockchain Technologymentioning

confidence: 99%

Blockchain based transactive energy systems for voltage regulation in active distribution networks

et al. 2020

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“…Chan et al 17 explore the feasibility of affiliating Ethereum addresses using transaction graph analytics where transaction data are transferred into a graph database and tags are collected from sources such as etherscan.io. Addresses are considered to be affiliated if they share a transaction.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, we propose an approach to link together addresses that are used to deploy smart contracts, which are produced by the same authors across different accounts, and in essence undermine pseudo‐anonymity. Previous research on de‐anonymization explored the affiliation of Bitcoin addresses by using out‐of‐network information such as IP addresses, 11,13 geo‐locations, 14 inner network information using graph analysis, 15 and Bitcoin address classification techniques 16,17 . We take an alternative approach and leverage stylometry techniques, widely used in the social sciences for attribution of literary texts to their corresponding authors.…”

Section: Introductionmentioning

confidence: 99%

De‐anonymizing Ethereum blockchain smart contracts through code attribution

2020

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SummaryBlockchain users are identified by addresses (public keys), which cannot be easily linked back to them without out‐of‐network information. This provides pseudo‐anonymity, which is amplified when the user generates a new address for each transaction. Since all transaction history is visible to all users in public blockchains, finding affiliation between related addresses undermines pseudo‐anonymity. Such affiliation information can be used to discriminate against addresses linked with undesired activities or can lead to de‐anonymization if out‐of‐network information becomes available.In this work, we propose an approach to undermine pseudo‐anonymity of blockchain transactions by linking together addresses that were used to deploy smart contracts, which were produced by the same authors. In our approach, we leverage stylometry techniques, widely used in the social science field for attribution of literary texts to their corresponding authors. The assumption underlying authorship attribution is the existence of a distinctive writing style, unique to an author and easily distinguishable from others. Drawing an analogy between literary text and smart contracts' source code, we explore the extent to which unique features of source code and byte code of Ethereum smart contracts can represent the coding style of smart contract developers. We show that even a small number of representative features leads to a sufficiently high accuracy in attributing smart contracts' code to its deployer's address. We further validate our approach on real‐world scammers' data and Ponzi scheme‐related contracts. Additionally, we provide an algorithm to extract distinctly contributing features per an entire dataset or per specific authors. We use this algorithm to extract and explore such features in our dataset and in the Ponzi scheme‐related dataset.

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“…As such, analysis of Ethereum networks might be even more acute than cryptocurrency price prediction. However, despite this high token activity, network structure of Ethereum transaction graph remains largely understudied [12,18]. Furthermore, to the best of our knowledge, there exist no studies of Ethereum that link crypto-token price analytics with the underlying Ethereum transaction graph.…”

Section: Introductionmentioning

confidence: 99%

Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of the Ethereum Graph?

Li¹,

Islambekov²,

Akcora³

et al. 2020

Proceedings of the 2020 SIAM International Conference on Data Mining

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Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world. In contrast to fiat currencies, all transactions of crypto-currencies and cryptotokens are permanently recorded on distributed ledgers and are publicly available. As a result, this allows us to construct a transaction graph and to assess not only its organization but to glean relationships between transaction graph properties and crypto price dynamics. The ultimate goal of this paper is to facilitate our understanding on horizons and limitations of what can be learned on cryptotokens from local topology and geometry of the Ethereum transaction network whose even global network properties remain scarcely explored. By introducing novel tools based on topological data analysis and functional data depth into Blockchain Data Analytics, we show that Ethereum network (one of the most popular blockchains for creating new crypto-tokens) can provide critical insights on price strikes of crypto-tokens that are otherwise largely inaccessible with conventional data sources and traditional analytic methods.

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Ethereum transaction graph analysis

Cited by 51 publications

References 3 publications

Blockchain based transactive energy systems for voltage regulation in active distribution networks

Blockchain based transactive energy systems for voltage regulation in active distribution networks

De‐anonymizing Ethereum blockchain smart contracts through code attribution

Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of the Ethereum Graph?

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