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
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 @article{title="SuPoolVisor: a visual analytics system for mining pool surveillance", %0 Journal Article TY - JOUR
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: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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