### Nice Attacks --- but What is the Cost? Computational Models for Cryptanalysis

Charles Bouillaguet

##### Abstract

This paper discusses the implications of choosing a computational model to study the cost of cryptographic attacks and therefore quantify how dangerous they are. This choice is often unconscious and the chosen model itself is usually implicit; but it has repercussions on security evaluations. We compare three reasonable computational models: $i$) the usual Random Access Machine (RAM) model; $ii$) the Expensive Memory Model'' explicitly introduced by several 3rd-round submissions to the Post-Quantum NIST competition (it states that a single access to a large memory costs as much as many local operations); $iii)$ the venerable VLSI model using the Area-Time cost measure. It is well-known that costs in the RAM model are lower that costs in the last two models. These have been claimed to be more realistic, and therefore to lead to more precise security evaluations. The main technical contribution of this paper is to show that the last two these models are incomparable. We identify a situation where the expensive memory model overestimates costs compared to the (presumably even more realistic) VLSI model. In addition, optimizing the cost in each model is a distinct objective that leads to different attack parameters, and raises the question of what is the best'' way to proceed for an eventual attacker. We illustrate these discrepancies by studying several generic attacks against hash function and Feistel networks in the three models.

Available format(s)
Category
Foundations
Publication info
Preprint. MINOR revision.
Keywords
complexity theorycryptanalysisfoundations
Contact author(s)
charles bouillaguet @ lip6 fr
History
Short URL
https://ia.cr/2022/197

CC BY

BibTeX

@misc{cryptoeprint:2022/197,
author = {Charles Bouillaguet},
title = {Nice Attacks --- but What is the Cost? Computational Models for Cryptanalysis},
howpublished = {Cryptology ePrint Archive, Paper 2022/197},
year = {2022},
note = {\url{https://eprint.iacr.org/2022/197}},
url = {https://eprint.iacr.org/2022/197}
}

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