CLC number: TP39
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-03-06
Cited: 0
Clicked: 5777
Citations: Bibtex RefMan EndNote GB/T7714
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, 2020, 21(4): 507-523.
@article{title="SuPoolVisor: a visual analytics system for mining pool surveillance",
author="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",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="4",
pages="507-523",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900532"
}
%0 Journal Article
%T SuPoolVisor: a visual analytics system for mining pool surveillance
%A Jia-zhi Xia
%A Yu-hong Zhang
%A Hui Ye
%A Ying Wang
%A Guang Jiang
%A Ying Zhao
%A Cong Xie
%A Xiao-yan Kui
%A Sheng-hui Liao
%A Wei-ping Wang
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 4
%P 507-523
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900532
TY - JOUR
T1 - SuPoolVisor: a visual analytics system for mining pool surveillance
A1 - Jia-zhi Xia
A1 - Yu-hong Zhang
A1 - Hui Ye
A1 - Ying Wang
A1 - Guang Jiang
A1 - Ying Zhao
A1 - Cong Xie
A1 - Xiao-yan Kui
A1 - Sheng-hui Liao
A1 - Wei-ping Wang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 4
SP - 507
EP - 523
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900532
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.
[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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