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CLC number: TP39

On-line Access: 2020-04-21

Received: 2019-09-28

Revision Accepted: 2020-02-02

Crosschecked: 2020-03-06

Cited: 0

Clicked: 4604

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jia-zhi Xia

https://orcid.org/0000-0003-4629-6268

Ying Zhao

https://orcid.org/0000-0002-4200-5200

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Frontiers of Information Technology & Electronic Engineering 

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SuPoolVisor: a visual analytics system for mining pool surveillance


Author(s):  Jia-zhi Xia, Yu-hong Zhang, Hui Ye, Ying Wang, Guang Jiang, Ying Zhao, Cong Xie, Xiao-yan Kui, Sheng-hui Liao, Wei-ping Wang

Affiliation(s):  School of Computer Science and Engineering, Central South University, Changsha 410083, China; more

Corresponding email(s):  xiajiazhi@csu.edu.cn, zhangyuhong@csu.edu.cn, zhaoying@csu.edu.cn

Key Words:  Bitcoin mining pool, Visual analytics, Transaction data, Visual reasoning, FinTech


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Jia-zhi Xia, Yu-hong Zhang, Hui Ye, Ying Wang, Guang Jiang, Ying Zhao, Cong Xie, Xiao-yan Kui, Sheng-hui Liao, Wei-ping Wang. SuPoolVisor: a visual analytics system for mining pool surveillance[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.1900532

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year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.1900532"
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Abstract: 
Cryptocurrencies represented by Bitcoin have fully demonstrated their advantages and great potential in payment and monetary systems during the last decade. The mining pool, which is considered the source of Bitcoin, is the cornerstone of market stability. The surveillance of the mining pool can help regulators effectively assess the overall health of Bitcoin and issues. However, the anonymity of mining-pool miners and the difficulty of analyzing large numbers of transactions limit in-depth analysis. It is also a challenge to achieve intuitive and comprehensive monitoring of multi-source heterogeneous data. In this study, we present SuPoolVisor, an interactive visual analytics system that supports surveillance of the mining pool and de-anonymization by visual reasoning. SuPoolVisor is divided into pool level and address level. At the pool level, we use a sorted stream graph to illustrate the evolution of computing power of pools over time, and glyphs are designed in two other views to demonstrate the influence scope of the mining pool and the migration of pool members. At the address level, we use a force-directed graph and a massive sequence view to present the dynamic address network in the mining pool. Particularly, these two views, together with the Radviz view, support an iterative visual reasoning process for de-anonymization of pool members and provide interactions for cross-view analysis and identity marking. Effectiveness and usability of SuPoolVisor are demonstrated using three cases, in which we cooperate closely with experts in this field.

SuPoolVisor:矿池监管可视分析系统


夏佳志1,张宇鸿1,叶慧1,汪颖1,蒋广1,赵颖1
解聪2,奎晓燕1,廖胜辉1,王伟平1
1中南大学计算机学院,中国长沙市,410083
2Facebook,美国纽约市,10003

摘要:在过去十年中,以比特币为代表的加密货币充分展示其在支付和货币系统中的巨大优势与潜力。矿池被认为是比特币的来源,也是市场稳定的基石。对矿池的监管可帮助监管机构有效评估比特币的整体健康状况。但是,矿池匿名性和分析海量交易的难度限制了更深入的分析。此外,对多源异构数据直观和全面的监管也是一个挑战。本文设计并实现一个交互式可视分析系统SuPoolVisor,它可对矿池进行可视化监管,并支持使用可视推理对矿池去匿名化。SuPoolVisor支持矿池和地址两个级别的分析。在矿池级别,使用排序的河流图呈现矿池算力随时间演变情况,并在其他两个视图中设计特殊图形以说明矿池的影响范围和矿池成员迁移。在地址级别,使用力导向图和大规模序列视图呈现矿池中的动态地址网络。特别地,这两个视图与Radviz视图的组合,可用于矿池成员去匿名化的迭代可视推理过程,这些视图都提供了用于跨视图分析和标识的交互功能。我们与该领域专家紧密合作完成3个真实案例,并在案例中证明SuPoolVisor的有效性和可用性。

关键词组:比特币矿池;可视分析;交易数据;可视推理;金融科技

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Aigner W, Miksch S, Schumann H, et al., 2011. Visualization of Time-Oriented Data. Springer, London, UK.

[2]Athey S, Parashkevov I, Sarukkai V, et al., 2016. Bitcoin Pricing, Adoption, and Usage: Theory and Evidence. Research Papers 3469, Stanford University, San Francisco, USA.

[3]Barkatullah J, Hanke T, 2015. Goldstrike 1: CoinTerra’s first-generation cryptocurrency mining processor for Bitcoin. IEEE Micro, 35(2):68-76.

[4]Belotti M, Kirati S, Secci S, 2018. Bitcoin pool-hopping detection. Proc IEEE 4th Int Forum on Research and Technology for Society and Industry, p.1-6.

[5]Bistarelli S, Santini F, 2017. Go with the Bitcoin flow, with visual analytics. Proc 12th Int Conf on Availability, Reliability and Security, Article 38.

[6]Böhme R, Christin N, Edelman B, et al., 2015. Bitcoin: economics, technology, and governance. J Econom Persp, 29(2):213-238.

[7]Bohr J, Bashir M, 2014. Who uses Bitcoin? An exploration of the Bitcoin community. Proc 12th Annual Int Conf on Privacy, Security and Trust, p.94-101.

[8]Chen HD, Chen W, Mei HH, et al., 2014. Visual abstraction and exploration of multi-class scatterplots. IEEE Trans Vis Comput Graph, 20(12):1683-1692.

[9]Chen SM, Li J, Andrienko G, et al., 2018. Supporting story synthesis: bridging the gap between visual analytics and storytelling. IEEE Trans Vis Comput Graph, 14(8):1.

[10]Chen W, Lao TY, Xia J, et al., 2016. Gameflow: narrative visualization of NBA basketball games. IEEE Trans Multim, 18(11):2247-2256.

[11q]Chen W, Huang ZS, Wu FR, et al., 2018a. Vaud: a visual analysis approach for exploring spatio-temporal urban data. IEEE Trans Vis Comput Graph, 24(9):2636-2648.

[12]Chen W, Xia J, Wang XM, et al., 2018b. RelationLines: visual reasoning of egocentric relations from heterogeneous urban data. ACM Trans Intell Syst Technol, 10(1):2.

[13]Chen W, Guo FZ, Han DM, et al., 2019. Structure-based suggestive exploration: a new approach for effective exploration of large networks. IEEE Trans Vis Comput Graph, 25(1):555-565.

[14]Di Battista G, Di Donato V, Patrignani M, et al., 2015. Bitconeview: visualization of flows in the Bitcoin transaction graph. Proc IEEE Symp on Visualization for Cyber Security, p.1-8.

[15]Fleder M, Kester MS, Pillai S, 2015. Bitcoin transaction graph analysis. https://arxiv.org/abs/1502.01657v1

[16]Gencer AE, Basu S, Eyal I, et al., 2018. Decentralization in Bitcoin and Ethereum networks. Proc 22nd Int Conf on Financial Cryptography and Data Security, p.439-457.

[17]Hoffman P, Grinstein G, Marx K, et al., 1997. DNA visual and analytic data mining. Proc 8th IEEE Visualization Conf, p.437-441.

[18]Isenberg P, Kinkeldey C, Fekete JD, 2017. Exploring entity behavior on the Bitcoin blockchain. Université Paris-Saclay, Paris, France.

[19]Jie L, Chen SM, Zhang K, et al., 2019. COPE: interactive exploration of co-occurrence patterns in spatial time series. IEEE Trans Vis Comput Graph, 25(8):2554-2567.

[20]Kim YB, Kim JG, Kim W, et al., 2016. Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PLoS ONE, 11(8):e0161197.

[21]Kinkeldey C, Fekete JD, Isenberg P, 2017. BitConduite: visualizing and analyzing activity on the Bitcoin network. Eurographics Conf on Visualization, p.3. https://diglib.eg.org:443/handle/10.2312/eurp20171160

[22]Kiran M, Stannett M, 2015. Bitcoin Risk Analysis. NEMODE Policy Paper, p.1-28.

[23]Kirsh D, 2009. Projection, problem space, and anchoring. Proc 31st Cognitive Science Society, p.2310-2315.

[24]Koshy P, Koshy D, McDaniel P, 2014. An analysis of anonymity in Bitcoin using P2P network traffic. Proc 18th Int Conf on Financial Cryptography and Data Security, p.469-485.

[25]Kroll JA, Davey ID, Felten EW, 2013. The economics of Bitcoin mining, or Bitcoin in the presence of adversaries. Proc 12th Workshop on the Economics of Information Security, p.1-21.

[26]Lewenberg Y, Bachrach Y, Sompolinsky Y, et al., 2015. Bitcoin mining pools: a cooperative game theoretic analysis. Proc Int Conf on Autonomous Agents and Multiagent Systems, p.919-927.

[27]Li J, Chen SM, Chen W, et al., 2020. Semantics-space-time cube. a conceptual framework for systematic analysis of texts in space and time. IEEE Trans Vis Comput Graph, 26(4):1789-1806.

[28]Liu MC, Shi JX, Li Z, et al., 2017. Towards better analysis of deep convolutional neural networks. IEEE Trans Vis Comput Graph, 23(1):91-100.

[29]Liu MC, Shi JX, Cao KL, et al., 2018. Analyzing the training processes of deep generative models. IEEE Trans Vis Comput Graph, 24(1):77-87.

[30]Liu SX, Cui WW, Wu YC, et al., 2014. A survey on information visualization: recent advances and challenges. Visual Comput, 30(12):1373-1393.

[31]Liu SX, Andrienko G, Wu YC, et al., 2018. Steering data quality with visual analytics: the complexity challenge. Vis Inform, 2(4):191-197.

[32]Liu ZC, Stasko J, Sullivan T, 2009. SellTrend: inter-attribute visual analysis of temporal transaction data. IEEE Trans Vis Comput Graph, 15(6):1025-1032.

[33]Luo XN, Yuan Y, Zhang KY, et al., 2019. Enhancing statistical charts: toward better data visualization and analysis. J Vis, 22(4):819-832.

[34]Luu L, Saha R, Parameshwaran I, et al., 2015. On power splitting games in distributed computation: the case of Bitcoin pooled mining. Proc 28th Computer Security Foundations Symp, p.397-411.

[35]McGinn D, Birch D, Akroyd D, et al., 2016. Visualizing dynamic Bitcoin transaction patterns. Big Data, 4(2):109-119.

[36]Mei HH, Chen W, Wei YT, et al., 2019. Rsatree: distribution-aware data representation of large-scale tabular datasets for flexible visual query. https://arxiv.org/abs/1908.02005

[37]Meiklejohn S, Orlandi C, 2015. Privacy-enhancing overlays in Bitcoin. Int Conf on Financial Cryptography and Data Security, p.127-141.

[38]Meiklejohn S, Pomarole M, Jordan G, et al., 2013. A fistful of Bitcoins: characterizing payments among men with no names. Proc Conf on Internet Measurement, p.127-140.

[39]Moore T, Christin N, 2013. Beware the middleman: empirical analysis of Bitcoin-exchange risk. Proc 17th Int Conf on Financial Cryptography and Data Security, p.25-33.

[40]Möser M, Böhme R, Breuker D, 2013. An inquiry into money laundering tools in the Bitcoin ecosystem. Proc APWG eCrime Researchers Summit, p.1-14.

[41]Nakamoto S, 2008. Bitcoin: a peer-to-peer electronic cash system. https://bitcoin.org/bitcoin.pdf

[42]Neudecker T, Hartenstein H, 2017. Could network information facilitate address clustering in Bitcoin? Proc Int Conf on Financial Cryptography and Data Security, p.155-169.

[43]Ober M, Katzenbeisser S, Hamacher K, 2013. Structure and anonymity of the Bitcoin transaction graph. Fut Int, 5(2):237-250.

[44]Ranshous S, Joslyn CA, Kreyling S, et al., 2017. Exchange pattern mining in the Bitcoin transaction directed hypergraph. Proc Int Conf on Financial Cryptography and Data Security, p.248-263.

[45]Ron D, Shamir A, 2013. Quantitative analysis of the full Bitcoin transaction graph. Proc Int Conf on Financial Cryptography and Data Security, p.248-263.

[46]Spagnuolo M, Maggi F, Zanero S, 2014. Bitiodine: extracting intelligence from the Bitcoin network. Proc 18th Int Conf on Financial Cryptography and Data Security, p.457-468.

[47]Vasek M, Moore T, 2015. There’s no free lunch, even using Bitcoin: tracking the popularity and profits of virtual currency scams. Proc 19th Int Conf on Financial Cryptography and Data Security, p.44-61.

[48]Vasek M, Thornton M, Moore T, 2014. Empirical analysis of denial-of-service attacks in the Bitcoin ecosystem. Proc Int Conf on Financial Cryptography and Data Security, p.57-71.

[49]Wang LQ, Liu Y, 2015. Exploring miner evolution in Bitcoin network. Proc 16th Int Conf on Passive and Active Network Measurement, p.290-302.

[50]Wang X, Cui ZW, Jiang L, et al., 2020. WordleNet: a visualization approach for relationship exploration in document collection. Tsinghua Sci Technol, 25(3):384-400.

[51]Wang XM, Chou JK, Chen W, et al., 2018. A utility-aware visual approach for anonymizing multi-attribute tabular data. IEEE Trans Vis Comput Graph, 24(1):351-360.

[52]Wang XM, Chen W, Chou JK, et al., 2019. GraphProtector: a visual interface for employing and assessing multiple privacy preserving graph algorithms. IEEE Trans Vis Comput Graph, 25(1):193-203.

[53]Wei JS, Shen ZQ, Sundaresan N, et al., 2012. Visual cluster exploration of web clickstream data. Proc IEEE Conf on Visual Analytics Science and Technology, p.3-12.

[54]Wu YC, Xie X, Wang JC, et al., 2019. ForVizor: visualizing spatio-temporal team formations in soccer. IEEE Trans Vis Comput Graph, 25(1):65-75.

[55]Xia JZ, Ye FJ, Zhou FF, et al., 2019. Visual identification and extraction of intrinsic axes in high-dimensional data. IEEE Access, 7:79565-79578.

[56]Xie C, Chen W, Huang XX, et al., 2014. VAET: a visual analytics approach for e-transactions time-series. IEEE Trans Vis Comput Graph, 20(12):1743-1752.

[57]Ying Z, Luo XB, Lin XR, et al., 2019. Visual analytics for electromagnetic situation awareness in radio monitoring and management. IEEE Trans Vis Comput Graph, 26(1):590-600.

[58]Yli-Huumo J, Ko D, Choi S, et al., 2016. Where is current research on blockchain technology?—a systematic review. PLoS ONE, 11(10):e0163477.

[59]Yue XW, Shu XH, Zhu XY, et al., 2019. Bitextract: interactive visualization for extracting Bitcoin exchange intelligence. IEEE Trans Vis Comput Graph, 25(1):162-171.

[60]Zeng W, Fu CW, Arisona SM, et al., 2017. A visual analytics design for studying rhythm patterns from human daily movement data. Vis Inform, 1(2):81-91.

[61]Zhao Y, Luo F, Chen MH, et al., 2019. Evaluating multi-dimensional visualizations for understanding fuzzy clusters. IEEE Trans Vis Comput Graph, 25(1):12-21.

[62]Zhao Y, Wang L, Li SJ, et al., 2020. A visual analysis approach for understanding durability test data of automotive products. ACM Trans Intell Syst Technol, 10(6):1-23.

[63]Zhou FF, Lin XR, Liu C, et al., 2019. A survey of visualization for smart manufacturing. J Vis, 22(2):419-435.

[64]Zhou ZG, Ye ZF, Liu YN, et al., 2017. Visual analytics for spatial clusters of air-quality data. IEEE Comput Graph Appl, 37(5):98-105.

[65]Zhou ZG, Meng LH, Tang C, et al., 2019. Visual abstraction of large scale geospatial origin-destination movement data. IEEE Trans Vis Comput Graph, 25(1):43-53.

[66]Zhou ZG, Zhang XL, Guo ZY, et al., 2020. Visual abstraction and exploration of large-scale geographical social media data. Neurocomputing, 376:244-255.

[67]Zhu MF, Chen W, Xia JZ, et al., 2019. Location2vec: a situation-aware representation for visual exploration of urban locations. IEEE Trans Intell Transp Syst, 20(10):3891-3990.

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