An empirical study of privacy-preserving blockchains with the transaction graph
The two most prominent blockchains, Bitcoin and Ethereum, offer very little privacy for their users. Any transaction reveals sender, recipient and exchanged amount. Privacy-preserving blockchains such as Monero, Zcash, Mimblewimble or Dash, alleviate this issue by hiding this information. Although most of these chains use well-established cryptographic methods, their implementations have been the subject of multiple attacks and vulnerabilities over the past, mostly due to empirical studies (e.g., Monero [1,2,3,4,5]).
In this project, we propose a novel approach to empirically study the privacy guarantees of such blockchains. The transaction graph has been recently used to model different privacy-preserving systems, but it was never used to study empirical properties. This work addresses that specifically. It consists in building the transaction graph from an existing blockchain and performing an empirical analysis, using the underlying properties of the graph.
Requirements: Interest in privacy-preserving blockchains, knowledge of at least one programming language, ease with data extraction and manipulation, and willingness to explore a graph-based data structure.
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 Christoph Egger, Russell W. F. Lai, Viktoria Ronge, Ivy K. Y. Woo, and Hoover H. F. Yin. 2022. On Defeating Graph Analysis of Anonymous Transactions. Proc. Priv. Enhancing Technol. 2022, 3 (2022), 538–557. DOI:https://doi.org/10.56553/popets-2022-0085