Bitcoin Transaction Graph Analysis
Not on the map. Why?
Abstract (arXiv)
Bitcoins have recently become an increasingly popular cryptocurrency through which users trade electronically and more anonymously than via traditional electronic transfers. Bitcoin's design keeps all transactions in a public ledger. The sender and receiver for each transaction are identified only by cryptographic public-key ids. This leads to a common misconception that it inherently provides anonymous use. While Bitcoin's presumed anonymity offers new avenues for commerce, several recent studies raise user-privacy concerns. We explore the level of anonymity in the Bitcoin system. Our approach is two-fold: (i) We annotate the public transaction graph by linking bitcoin public keys to "real" people - either definitively or statistically. (ii) We run the annotated graph through our graph-analysis framework to find and summarize activity of both known and unknown users.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Feb 2015.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 2; 2023: 3; 2024: 1.
6 papers.
- 2022Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensicscited by 5
- 2023Graph Mining for Cybersecurity: A Surveycited by 5
- 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approachcited by 3
- 2023NFTVis: Visual Analysis of NFT Performancecited by 1
- 2023From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrencycited by 1
- 2022Toward Intention Discovery for Early Malice Detection in Bitcoincited by 0
- 2024Can Graph Neural Networks Expose Training Data Properties? An Efficient Risk Assessment Approach6 Nov 2024
- 2023From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency26 Sep 2023
- 2023NFTVis: Visual Analysis of NFT Performance5 Jun 2023
- 2023Graph Mining for Cybersecurity: A Survey2 Apr 2023
- 2022Toward Intention Discovery for Early Malice Detection in Bitcoin24 Sep 2022
- 2022Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics7 Jun 2022