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Incentivizing Blockchain Forks via Whale Transactions

  • Conference paper
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Financial Cryptography and Data Security (FC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10323))

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Abstract

Bitcoin’s core innovation is its solution to double-spending, called Nakamoto consensus. This provides a probabilistic guarantee that transactions will not be reversed or redirected, presuming that it is improbable for an attacker to obtain a majority of mining power in the network. However, this guarantee can be undermined when miners are assumed to be rational, and hence venal. Accordingly, we present the whale attack, in which a minority attacker increases her chances of double-spending by incentivizing miners to subvert the consensus protocol and to collude via whale transactions, which are bribery transactions carrying anomalously large fees. We analyze the expected cost to carry out the attack with success probability 1, and simulate the attack under realistic system parameters. Our results show that double-spend attacks, conventionally thought to be impractical for minority attackers, can actually be financially feasible and worthwhile under the whale attack. Perhaps more importantly, this work demonstrates that rationality should not underestimated when evaluating the security of cryptocurrencies.

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Notes

  1. 1.

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Acknowledgments

We thank Elijah Soriah and Andrew Miller for their valuable feedback, and the faculty and students of the CAAR REU program for the wonderful experience. This work is funded by NSF Research Experience for Undergraduates (REU) Grant CNS-1560193.

Author information

Authors and Affiliations

  1. Arizona State University, Chandler, USA

    Kevin Liao

  2. University of Maryland, College Park, USA

    Jonathan Katz

Authors
  1. Kevin Liao
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  2. Jonathan Katz
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Corresponding author

Correspondence to Kevin Liao .

Editor information

Editors and Affiliations

  1. Leibniz Universität Hannover, Hannover, Germany

    Michael Brenner

  2. New Jersey Institute of Technology, Newark, New Jersey, USA

    Kurt Rohloff

  3. New York University, New York, New York, USA

    Joseph Bonneau

  4. University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Andrew Miller

  5. University of Luxembourg, Luxembourg, Luxembourg

    Peter Y.A. Ryan

  6. University of Melbourne, Parkville, Victoria, Australia

    Vanessa Teague

  7. University of Stirling, Stirling, United Kingdom

    Andrea Bracciali

  8. University of Trento, Trento, Italy

    Massimiliano Sala

  9. University of Trento, Trento, Italy

    Federico Pintore

  10. Agari Inc., San Mateo, California, USA

    Markus Jakobsson

Appendices

A Whale Attack Algorithm

figure a

B Full Table for Sufficient Values of Whale Transactions

See Tables 1, 2 and 3.

Table 1. The value of \(\delta \) (whale attack premium) that makes whale mining more profitable than honest mining, as a function of the lead of the original branch at the start of the race phase z, Alice’s mining power \(\alpha \) (rows), and m’s mining power \(\beta _m\) (columns). For \(z=0\), \(\delta \) is always equal to 0.
Full size table

C Bitcoin Mining Distribution Snapshot

Table 2. Distribution of mining power among the ten largest pools (95% of the network) from July 30-August 2, 2016 (Source: https://blockchain.info/pools).
Full size table

D Simulated Cost of Whale Attack

Table 3. The simulated attack cost (sum of \(\delta \)s) under different parameters of the whale mining power q and the lead of the original branch at the start of the race phase z. The values shown are averages across \(10^6\) simulations for each pair of q and z.
Full size table

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Liao, K., Katz, J. (2017). Incentivizing Blockchain Forks via Whale Transactions. In: Brenner, M., et al. Financial Cryptography and Data Security. FC 2017. Lecture Notes in Computer Science(), vol 10323. Springer, Cham. https://doi.org/10.1007/978-3-319-70278-0_17

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  • DOI: https://doi.org/10.1007/978-3-319-70278-0_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70277-3

  • Online ISBN: 978-3-319-70278-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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