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Comparing Key Rank Estimation Methods

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Smart Card Research and Advanced Applications (CARDIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13820))

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Abstract

Recent works on key rank estimation methods claim that algorithmic key rank estimation is too slow, and suggest two new ideas: replacing repeat attacks with simulated attacks (PS-TH-GE rank estimation), and a shortcut rank estimation method that works directly on distinguishing vector distributions (GEEA). We take these ideas and provide a comprehensive comparison between them and a performant implementation of a classical, algorithmic ranking approach, as well as some earlier work on estimating distinguisher distributions. Our results show, in contrast to the recent work, that the algorithmic ranking approach outperforms GEEA, and that simulation based ranks are unreliable.

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Notes

  1. 1.

    It is important to distinguish between the average over multiple key rank experiments, and the expected key rank aka Guessing Entropy. Both metrics capture the average behaviour, but the latter requires information about the entire key rank distribution.

  2. 2.

    The style of experiments in [20] is in line with the single byte experiments in previous work such as [17].

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Acknowledgements

Rebecca Young has been funded by an NCSC studentship. Elisabeth Oswald was supported in part by the ERC via the grant SEAL (project reference 725042).

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Authors and Affiliations

  1. Department of Computer Science, University of Bristol, Bristol, UK

    Rebecca Young & Elisabeth Oswald

  2. PQShield, Bristol, UK

    Luke Mather

  3. Digital Age Research Center (D!ARC), University of Klagenfurt, Klagenfurt, Austria

    Elisabeth Oswald

Authors
  1. Rebecca Young
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  2. Luke Mather
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  3. Elisabeth Oswald
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Correspondence to Elisabeth Oswald .

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Editors and Affiliations

  1. Radboud University, Nijmegen, The Netherlands

    Ileana Buhan

  2. NXP Semiconductors, Gratkorn, Austria

    Tobias Schneider

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Young, R., Mather, L., Oswald, E. (2023). Comparing Key Rank Estimation Methods. In: Buhan, I., Schneider, T. (eds) Smart Card Research and Advanced Applications. CARDIS 2022. Lecture Notes in Computer Science, vol 13820. Springer, Cham. https://doi.org/10.1007/978-3-031-25319-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-25319-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25318-8

  • Online ISBN: 978-3-031-25319-5

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