Date of Award

Winter 12-2017

Embargo Period

6-19-2018

Degree Type

Thesis

Degree Name

Master of Science in Information Technology (MSIT)

Department

Electrical and Computer Engineering

Advisor(s)

Nicolas Christin

Abstract

We investigate to which degree one could trace Bitcoin transactions and characterize purchasing behavior of online anonymous marketplaces by exploiting side channels. Using a list of addresses found by the FBI on Silk Road servers, and information on the marketplace's official guides, we infer the role played by each address in the list and classify them based on heuristics. We then attempt to trace Bitcoin transactions and show that the anonymity set size is greatly reduced using product review data and the address classification performed on the previous step. Finally, using clustering techniques based on transaction graph analysis, we assign addresses into user wallets, then group these wallets together based on spending patterns, to be able to characterize purchasing behavior.

Media Format

flash_audio

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Available for download on Tuesday, June 19, 2018

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