CryptoDB
Quantum Speed-Up for Multidimensional (Zero Correlation) Linear Distinguishers
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| Conference: | ASIACRYPT 2023 |
| Abstract: | This paper shows how to achieve a quantum speed-up for multidimensional (zero correlation) linear distinguishers. A previous work by Kaplan et al. has already shown a quantum quadratic speed-up for one-dimensional linear distinguishers. However, classical linear cryptanalysis often exploits multidimensional approximations to achieve more efficient attacks, and in fact it is highly non-trivial whether Kaplan et al.'s technique can be extended into the multidimensional case. To remedy this, we investigate a new quantum technique to speed-up multidimensional linear distinguishers. Firstly, we observe that there is a close relationship between the subroutine of Simon's algorithm and linear correlations via Fourier transform. Specifically, a slightly modified version of Simon's subroutine, which we call Correlation Extraction Algorithm (CEA), can be used to speed-up multidimensional linear distinguishers. CEA also leads to a speed-up for multidimensional zero correlation distinguishers, as well as some integral distinguishers through the correspondence of zero correlation and integral properties shown by Bogdanov et al.~and Sun et al. Furthermore, we observe possibility of a more than quadratic speed-ups for some special types of integral distinguishers when multiple integral properties exist. Especially, we show a single-query distinguisher on a 4-bit cell SPN cipher with the same integral property as 2.5-round AES. Our attacks are the first to observe such a speed-up for classical cryptanalytic techniques without relying on hidden periods or shifts. By replacing the Hadamard transform in CEA with the general quantum Fourier transform, our technique also speeds-up generalized linear distinguishers on an arbitrary finite abelian group. |
BibTeX
@inproceedings{asiacrypt-2023-33404,
title={Quantum Speed-Up for Multidimensional (Zero Correlation) Linear Distinguishers},
publisher={Springer-Verlag},
author={Akinori Hosoyamada},
year=2023
}