.png){kind=icon}
.png){kind=icon}
Return
Quantum Truncated Differential Attacks Using Convolutions
.png){kind=icon}
OA
AI
DOI：10.46586/tosc.v2026.i1.148-188.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper focuses on quantum key-recovery attacks on block ciphers.Previous works on quantum differential and truncated differential attacks like [Kaplanet al., ToSC 2016] have shown that classical algorithms for key-recovery, typicallybased on generating differential pairs and sieving them, can be accelerated by up to aquadratic speedup using variants of quantum search, quantum amplitude amplification,and quantum collision-finding.In this paper, we introduce a new quantum truncated differential key-recovery attack,which leverages the quantum convolution algorithm introduced in [Schrottenloher,CRYPTO 2022] and previously used in linear cryptanalysis. We adapt this algorithmto the case of differential cryptanalysis, by rewriting the probability of a differentialof ann-bit cipher as a convolution of functions with2n-bit input. We then constructa quantum state whose amplitudes encode the probability of the differential fordifferent key guesses, and use this as the starting point of a quantum search. Insome cases (although not on practical ciphers so far), the speedup is better thanquadratic compared to classical attacks. We also extend the framework to related-keydifferential attacks.We give applications to a 9-round attack on QARMAv2-64 adapted from [Ahmadianet al., DCC 2024] and a 12-round related-key attack on AES-256 from [Boura et al.,CRYPTO 2023], which show improvement over classical attacks and over Kaplanet al.'s strategy when taking into account the amount of memory and the type ofquantum memory used (as our attack requires only quantum-accessible classicalmemory
Keywords:
Quantum Cryptanalysis
Truncated Differential Cryptanalysis
Discreteconvolution
Quantum Fourier Transform
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
2.2
Papers:
21
Citations:
1.1K