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Short Paper: An Empirical Analysis of Monero Cross-chain Traceability

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11598))

Abstract

Monero is a privacy-centric cryptocurrency that makes payments untraceable by adding decoys to every real input spent in a transaction. Two studies from 2017 found methods to distinguish decoys from real inputs, which enabled traceability for a majority of transactions. Since then, a number protocol changes have been introduced, but their effectiveness has not yet been reassessed. Furthermore, little is known about traceability of Monero transactions across hard fork chains. We formalize a new method for tracing Monero transactions, which is based on analyzing currency hard forks. We use that method to perform a (passive) traceability analysis on data from the Monero, MoneroV and Monero Original blockchains and find that only a small amount of inputs are traceable. We then use the results to estimate the effectiveness of known heuristics for recent transactions and find that they do not significantly outperform random guessing. Our findings suggest that Monero is currently mostly immune to known passive attack vectors and resistant to tracking and tracing methods applied to other cryptocurrencies.

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Notes

  1. 1.

    https://github.com/oerpli/MONitERO

  2. 2.

    https://github.com/monero-project/monero/pull/1996/files.

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Acknowledgments

This work was partly funded by the European Commission through the project TITANIUM (Project ID: 740558) and by Austrian Research Promotion Agency (FFG) through the project VIRTCRIME (Project ID: 860672)

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Correspondence to Abraham Hinteregger .

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© 2019 International Financial Cryptography Association

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Hinteregger, A., Haslhofer, B. (2019). Short Paper: An Empirical Analysis of Monero Cross-chain Traceability. In: Goldberg, I., Moore, T. (eds) Financial Cryptography and Data Security. FC 2019. Lecture Notes in Computer Science(), vol 11598. Springer, Cham. https://doi.org/10.1007/978-3-030-32101-7_10

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  • DOI: https://doi.org/10.1007/978-3-030-32101-7_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32100-0

  • Online ISBN: 978-3-030-32101-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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