Ethereum Analysis via Node Clustering

Sun, Hanyi; Ruan, Na; Liu, Hanqing

doi:10.1007/978-3-030-36938-5_7

Cited by 23 publications

(14 citation statements)

References 12 publications

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“…One is using machine learning and node embedding methods to cluster transaction behaviour patterns or user accounts with similar characteristics. Sun et al [13] first applied the node embedding algorithm to the clustering of Ethernet accounts. Hu et al [7] designed a transaction-based classification detection method for Ethereum smart contracts by summarizing contract transaction behavior patterns.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Address Linkability in Tornado Cash on Ethereum

Tang

Zhang

et al. 2022

Communications in Computer and Information Science

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Tornado Cash, the most popular non-custodial coin mixer on Ethereum, is widely used to protect the privacy of addresses. However, some inappropriate transaction behaviors in Tornado Cash mixing mechanism lead to the risk of privacy leakage. More specifically, the malicious attackers can link multiple addresses of the same users according to the transaction data. Motivated by the above problem, this paper systematically analyzes the privacy issues of Tornado Cash for the first time. In this paper, we give the macroscopic analysis of Tornado Cash based on the on-chain data and formalize two types of transaction patterns. Focus on the presented transaction patterns, we propose three heuristic clustering rules to link the users’ addresses, which reduce the size of users’ anonymity set. Finally, we perform the experiment on real Tornado Cash transaction data to describe the effectiveness of the proposed clustering rules.

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Section: Related Workmentioning

confidence: 99%

Analysis of Address Linkability in Tornado Cash on Ethereum

Tang

Zhang

et al. 2022

Communications in Computer and Information Science

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“…Compared with the previous two works, this work implements a theoretical model and enriches the algorithm, and experiments are carried out to verify each part of the model. H. Sun et al [31] used a node embedding algorithm to calculate the eigenvector representation of external self-owned account nodes and intelligent contract account nodes in Ethereum and visualized the clustering result after clustering these nodes into groups based on the eigenvector. Chen et al [32] constructed three kinds of networks for analysis: money flow graphs (MFGs), smart contract creation graphs (CCGs), and smart contract invocation graphs (CIGs).…”

Section: Related Workmentioning

confidence: 99%

EOS.IO blockchain data analysis

et al. 2021

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Cryptocurrency is a relatively mature application of blockchain technology. The openness of transaction records provides researchers with the opportunity to analyse and compare various cryptocurrencies. The EOS public chain based on EOS. IO supports millions of transactions per second, with billions of transactions, and provides data analysts with a large quantity of analysable transaction data. Combined with the Ethereum platform data of the same period, this paper focuses on the transaction data in the EOS.IO blockchain and analyses the data in the Ethereum and EOS.IO chains from a complex network perspective. By constructing cumulative networks and time-slicing methods, constructing transaction networks of different scales, and dynamically analysing the laws of transaction network changes over time, we find that many transactions, such as transaction volume and transaction relationships, exhibit heavy-tail characteristics and conform to a power-law distribution. In particular, with the change in time and the growth in network scale, the power-law distribution is time-invariant. Our research can verify and predict the progress of blockchain development. Through graph analysis, we also obtained some other observations and discovered some interesting mathematical characteristics that explain the actual interactions that occurred on the blockchain.

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“…Multiple works reported accounts forming communities, i.e. groups of entities that interact frequently with each other [6,32]. While the communities change over time, preserving them can significantly increase performance of a sharded blockchain due to a reduced number of cross-shard transactions [15].…”

Section: Observationsmentioning

confidence: 99%

Shard scheduler

Król

Ascigil

Rene

et al. 2021

Proceedings of the 3rd ACM Conference on Advances in Financial Technologies

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We propose Shard Scheduler, a system for object placement and migration in account-based sharded blockchains. Our system calculates optimal placement and decides on object migrations across shards. It supports complex multi-account transactions caused by smart contracts. Placement and migration decisions made by Shard Scheduler are fully deterministic, verifiable, and can be made part of the consensus protocol. Shard Scheduler reduces the number of costly cross-shard transactions, ensures balanced load distribution and maximizes the number of processed transactions for the blockchain as a whole. To this end, it leverages a novel incentive model motivating miners to maximize the global throughput of the entire blockchain rather than the throughput of a specific shard. In our simulations, Shard Scheduler can reduce the number of costly cross-shard transactions by half while ensuring equal load and increasing throughput more than 2 fold when using 60 shards. We also implement and evaluate Shard Scheduler on Chainspace, more than doubling its throughput and reducing user-perceived latency by 70% when using 10 shards. CCS CONCEPTS• Security and privacy → Distributed systems security.

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Ethereum Analysis via Node Clustering

Cited by 23 publications

References 12 publications

Analysis of Address Linkability in Tornado Cash on Ethereum

Analysis of Address Linkability in Tornado Cash on Ethereum

EOS.IO blockchain data analysis

Shard scheduler

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