Comparing Key Rank Estimation Methods

Rebecca Young; Luke Mather; Elisabeth Oswald

doi:10.1007/978-3-031-25319-5_10

Comparing Key Rank Estimation Methods

Rebecca Young, Luke Mather, Elisabeth Oswald^*

^*Corresponding author for this work

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Smart Card Research and Advanced Applications - 21st International Conference, CARDIS 2022, Revised Selected Papers
Editors	Ileana Buhan, Tobias Schneider
Publisher	Springer
Pages	188-204
Number of pages	17
ISBN (Electronic)	9783031253195
ISBN (Print)	9783031253188
DOIs	https://doi.org/10.1007/978-3-031-25319-5_10
Publication status	Published - 29 Jan 2023
Event	21st International Conference on Smart Card Research and Advanced Applications, CARDIS 2022 - Birmingham, United Kingdom Duration: 7 Nov 2022 → 9 Nov 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13820
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Smart Card Research and Advanced Applications, CARDIS 2022
Country/Territory	United Kingdom
City	Birmingham
Period	7/11/22 → 9/11/22

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Estimation
Key rank

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-25319-5_10Licence: None: All rights reserved

Cite this

Young, R., Mather, L., & Oswald, E. (2023). Comparing Key Rank Estimation Methods. In I. Buhan, & T. Schneider (Eds.), Smart Card Research and Advanced Applications - 21st International Conference, CARDIS 2022, Revised Selected Papers (pp. 188-204). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13820). Springer. https://doi.org/10.1007/978-3-031-25319-5_10

Young, Rebecca ; Mather, Luke ; Oswald, Elisabeth. / Comparing Key Rank Estimation Methods. Smart Card Research and Advanced Applications - 21st International Conference, CARDIS 2022, Revised Selected Papers. editor / Ileana Buhan ; Tobias Schneider. Springer, 2023. pp. 188-204 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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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Young, R, Mather, L & Oswald, E 2023, Comparing Key Rank Estimation Methods. in I Buhan & T Schneider (eds), Smart Card Research and Advanced Applications - 21st International Conference, CARDIS 2022, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13820, Springer, pp. 188-204, 21st International Conference on Smart Card Research and Advanced Applications, CARDIS 2022, Birmingham, United Kingdom, 7/11/22. https://doi.org/10.1007/978-3-031-25319-5_10

Comparing Key Rank Estimation Methods. / Young, Rebecca; Mather, Luke; Oswald, Elisabeth.
Smart Card Research and Advanced Applications - 21st International Conference, CARDIS 2022, Revised Selected Papers. ed. / Ileana Buhan; Tobias Schneider. Springer, 2023. p. 188-204 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13820).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Oswald, Elisabeth

N1 - Funding Information: 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). Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023/1/29

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N2 - 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.

AB - 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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Young R, Mather L, Oswald E. Comparing Key Rank Estimation Methods. In Buhan I, Schneider T, editors, Smart Card Research and Advanced Applications - 21st International Conference, CARDIS 2022, Revised Selected Papers. Springer. 2023. p. 188-204. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-25319-5_10

Comparing Key Rank Estimation Methods

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this