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Demystifying Ethereum account diversity: observations, models and analysis

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Abstract

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart contracts. These massive accounts can be divided into diverse categories, such as miners, tokens, and exchanges, which is termed as account diversity in this paper. The benefit of investigating diversity are multi-fold, including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal activities. Unfortunately, the exploration of blockchain account diversity remains scarce. Even the most relevant studies, which focus on the deanonymization of the accounts on Bitcoin, can hardly be applied on Ethereum since their underlying protocols and user idioms are different. To this end, we present the first attempt to demystify the account diversity on Ethereum. The key observation is that different accounts exhibit diverse behavior patterns, leading us to propose the heuristics for classification as the premise. We then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation (MLE). We collect real-world data through extensive efforts to evaluate our proposed method and show its effectiveness. Furthermore, we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage actions. As for the former, we validate two sweeping statements reliably: (1) standalone miners are gradually replaced by the mining pools and cooperative miners; (2) transactions related to the mining pool and exchanges take up a large share of the total transactions. The latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

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Acknowledgements

This work was supported by Key-Area Research and Development Program of Guangdong Province (2020B0101090005) and the National Natural Science Foundation of China (Grant No. 62072197).

Author information

Authors and Affiliations

  1. Clusters and Grid Computing Lab, National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab, Wuhan, 430074, China

    Chaofan Wang, Xiaohai Dai, Jiang Xiao, Chenchen Li, Ming Wen & Hai Jin

  2. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Chaofan Wang, Xiaohai Dai, Jiang Xiao, Chenchen Li & Hai Jin

  3. School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Ming Wen

  4. Centre for Distributed and High Performance Computing, School of Information Technologies, The University of Sydney, Camperdown, New South Wales, 2006, Australia

    Bingbing Zhou

Authors
  1. Chaofan Wang
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  2. Xiaohai Dai
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  3. Jiang Xiao
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  4. Chenchen Li
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  5. Ming Wen
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  6. Bingbing Zhou
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  7. Hai Jin
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Corresponding author

Correspondence to Jiang Xiao.

Additional information

Chaofan Wang received his BS degree from Huazhong University of Science and Technology (HUST), China in 2017. He is currently pursuing his MS degree in School of Computer Science and Technology from HUST. His current research interests include blockchain system and data mining.

Xiaohai Dai received his MS degree in School of Computer Science and Technology from Huazhong University of Science and Technology (HUST), China in 2017. He is currently pursuing his PhD degree in School of Computer Science and Technology from HUST. His current research interests include blockchain and distributed system.

Jiang Xiao is currently an associate professor in School of Computer Science and Technology at Huazhong University of Science and Technology (HUST), China. She received her BS degree from HUST in 2009 and the PhD degree from Hong Kong University of Science and Technology (HKUST), China in 2014. She has been engaged in research on blockchain, distributed computing, wireless indoor localization, and smart sensing. Jiang has directed and participated in many research and deelopment projects and grants from funding agencies such as National Natural Science Foundation of China (NSFC), Hong Kong Research Grant Council (RGC), China, Hong Kong Innovation and Technology Commission (ITC), China, and industries like Huawei, Tencent and Intel, and been invited by NSFC in reviewing research projects. Her awards include CCF-Intel Young Faculty Research Program 2017, Hubei Downlight Program 2018, ACM Wuhan Rising Star Award 2019, and Best Paper Awards from IEEE ICPADS/GLOBECOM/GPC.

Chenchen Li received his BS degree from Zhongnan Unversity of Economics and Law, China. He is currently working toward the PhD degree in the CGCL, Huazhong University of science and technology, China. His research interests include block-chain, data mining and finance security.

Ming Wen is now an associate professor at the School of Cyber Science and Engineering, Huazhong University of Science and Technology, China. He received his PhD from the department of computer science and engineering at the Hong Kong University of Science and Technology (HKUST), China. His research interests include program analysis, fault localization & repair, and software security. His research work has been regularly published in top conferences and journals in the research communities of software engineering, including ICSE, FSE, ASE, TSE and EMSE and so on. More information about him can be found at: https://justinwm.github.io.

Bingbing Zhou is currently an associate professor in School of Computer Science and the theme leader for distributed computing applications in the Centre for Distributed and High Performance Computing at the University of Sydney, Australia. He received his BS degree in 1982 from Nanjing Institute of Technology, China and his PhD degree in computer science in 1989 from Australian National University, Australia. His research interests include parallel/distributed computing, cloud/edge computing, parallel algorithms, IoT and bioinformatics. He has a number of publications in leading international journals and conferences. His research has been funded by the Australian Research Council through several Discovery Project grants.

Hai Jin is a Professor of computer science and engineering at Huazhong University of Science and Technology (HUST), China. Jin received his PhD in computer engineering from HUST in 1994. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz, Germany. Jin worked at The University of Hong Kong, China between 1998 and 2000, and as a visiting scholar at the University of Southern California, US between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. Jin is the chief scientist of ChinaGrid, the largest grid computing project in China, and the chief scientists of National 973 Basic Research Program Project of Virtualization Technology of Computing System, and Cloud Security. Jin is a Fellow of the IEEE and a member of the ACM. He has co-authored 15 books and published over 700 research papers. His research interests include computer architecture, virtualization technology, cluster computing and cloud computing, peer-to-peer computing, network storage, and network security.

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Wang, C., Dai, X., Xiao, J. et al. Demystifying Ethereum account diversity: observations, models and analysis. Front. Comput. Sci. 16, 164505 (2022). https://doi.org/10.1007/s11704-021-0221-3

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