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A fistful of Bitcoins: characterizing payments among men with no names

Published: 23 March 2016 Publication History

Abstract

Bitcoin is a purely online virtual currency, unbacked by either physical commodities or sovereign obligation; instead, it relies on a combination of cryptographic protection and a peer-to-peer protocol for witnessing settlements. Consequently, Bitcoin has the unintuitive property that while the ownership of money is implicitly anonymous, its flow is globally visible. In this paper we explore this unique characteristic further, using heuristic clustering to group Bitcoin wallets based on evidence of shared authority, and then using re-identification attacks (i.e., empirical purchasing of goods and services) to classify the operators of those clusters. From this analysis, we consider the challenges for those seeking to use Bitcoin for criminal or fraudulent purposes at scale.

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Published In

cover image Communications of the ACM
Communications of the ACM  Volume 59, Issue 4
April 2016
87 pages
ISSN:0001-0782
EISSN:1557-7317
DOI:10.1145/2907055
  • Editor:
  • Moshe Y. Vardi
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 March 2016
Published in CACM Volume 59, Issue 4

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  • (2024)Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A SurveyAlgorithms10.3390/a1705020117:5(201)Online publication date: 9-May-2024
  • (2024)JoinDetect: A Data-Led Machine Learning Approach to Detection of Coinjoin TransactionsProceedings of the 2024 International Conference on Information Technology for Social Good10.1145/3677525.3678633(5-13)Online publication date: 4-Sep-2024
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  • (2024)PriDe CT: Towards Public Consensus, Private Transactions, and Forward Secrecy in Decentralized Payments2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00170(3904-3922)Online publication date: 19-May-2024
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