报告题目：数字货币的监管措施
报告时间：2021年10月7日(周四) 8:00-10:00
报告地点：腾讯会议 ID: 882 4461 9458
报告人： 尚广志（佛罗里达州立大学）
主办单位：新黄金城667733新黄金城667733
报告人简介：
Guangzhi Shang is Jim Moran Associate Professor of Operations Management in the Department of Business Analytics, Information Systems, and Supply Chain at Florida State University. His research has been published in Production and Operations Management (POM), Journal of Operations Management (JOM), and Decision Sciences (DS), among others, and recognized by best paper awards at POM, JOM, and POM Society’s College of Operational Excellence. He serves as the co-Department-Editor for the Empirical Research Methods Department at JOM and for the Retail Operations Department at DS. His review service is recognized by the 2019 outstanding reviewer award of DS and the 2018 best reviewer award of the Journal of Operations Management. He was also nominated for the best reviewer for POM and best associate editor for JOM. He co-produces a column together with Mike Galbreth and Mark Ferguson in the Reverse Logistics Magazine named “View from Academia,” aimed at disseminating fresh-off-the-press academic knowledge among industry professionals dealing with consumer returns.
内容简介：
Bitcoin is a cryptocurrency whose transactions are recorded ona distributed, openly accessible ledger. On the Bitcoin Blockchain, an owning entity’s real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for deanonymizing the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilized a sample of 957 entities (with ≈385 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 12 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm with default parameters, we achieve a mean cross validation accuracy of 80.42% and F1-score of ≈79.64%. We show two examples, one where we predict on a set of 22 clusters that are suspected to be related to cybercriminal activities, and another where we classify 153,293 clusters to provide an estimation of the activity on the Bitcoin ecosystem. We discuss the potential applications of our method for organizational regulation and compliance, societal implications, outline study limitations, and propose future research directions. A prototype implementation of our method for organizational use is included in the appendix.
