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Möser, M. (2022). Cryptocurrency privacy in practice. Unpublished Ph.D. Dissertation, Princeton University. 
Added by: Rucknium (2/23/22, 3:40 PM)   
Resource type: Thesis/Dissertation
BibTeX citation key: 2022
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Categories: Monero-focused
Creators: Möser, Narayanan
Publisher: Princeton University
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Attachments   Moeser_princeton_0181D_13955.pdf [13/349] URLs   http://arks.prince ... 435/dsp01dj52w7839
Cryptocurrencies like Bitcoin balance privacy and transparency goals. In these systems, all transactions are public by design, allowing nodes in a decentralized network to validate them. However, this reveals many potentially privacy-sensitive transaction details of users, whose privacy is only protected by varying degrees of pseudonymity. Understanding the privacy cryptocurrencies provide in practice is hence important for users transacting in them as well as law enforcement or regulatory agencies concerned about their illicit use. This thesis explores privacy in cryptocurrencies with differing transparency goals. We find that cryptocurrency privacy is inherently interdependent, as some users' behavior can negatively affect other users' privacy. Furthermore, privacy-sensitive disclosures from a few users can impact the privacy provided by the system at large. Yet, transparency need not come at the expense of privacy, and we show how it could be useful to impede illicit activity. First, analyzing the early use of the privacy-focused cryptocurrency Monero, we find that some users’ decision to forgo optional privacy protections obfuscating which coins they spend actively reduced those privacy provisions for others who opted for increased privacy. Furthermore, the obfuscation mechanisms employed by the Monero wallet did not match users’ actual behavior well, negatively affecting the privacy of all transactions. Next, we turn to address clustering, a blockchain analysis technique essential to understanding how Bitcoin is used in practice. As Bitcoin users can use many addresses, increasing their privacy, address clustering aims at revealing all addresses under a user’s control. However, current techniques haven't been rigorously evaluated or optimized. Using ground truth data extracted from the blockchain based on users’ privacy-compromising behavior, we build new models to identify users’ addresses with high accuracy and use them to create enhanced clusterings. Finally, we consider a system intentionally designed to aid law enforcement goals of deterring money laundering and other financial crime. Public blacklists of coins involved in illicit activity can incentivize individual users to reject payments that can be traced back to listed coins, impeding money laundering attempts. We discuss how blacklisting would change the Bitcoin ecosystem and provide a theoretical and empirical evaluation of different tainting policies.
Not sure if there is anything useful here beyond what was examined in Moser et al. (2018). Will check later.
Added by: Rucknium  Last edited by: Rucknium
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