Description
Time-Memory Tradeoff (TMTO) attacks on stream ciphers are a serious security threat and the resistance to this class of attacks is an important criterion in the design of a modern stream cipher. TMTO attacks are especially effective against stream ciphers where a variant of the TMTO attack can make use of multiple data to reduce the off-line and the on-line time complexities of the attack (given a fixed amount of memory).<br/> In this talk we present a new approach to TMTO attacks against stream ciphers using a publicly known initial value (IV): We suggest not to treat the IV as part of the secret key material (as done in current attacks), but rather to choose in advance some IVs and apply a TMTO attack to streams produced using these IVs. We show that while the obtained tradeoff curve is identical to the curve obtained by the current approach, the new technique allows to mount the TMTO attack in a larger variety of settings. For example, if both the secret key and the IV are of length $n$, it is possible to mount an attack with data, time, and memory complexities of 2^{4n/5}, while in the current approach, either the time complexity or the memory complexity is not less than 2^n. We conclude that if the IV length of a stream cipher is less than 1.5 times the key length, there exists an attack on the cipher with data, time, and memory complexities less than the complexity of exhaustive key search.<br/> This is a joint work with Nathan Keller.
Next sessions
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Key Attack on the ACDGV Matrix Encryption Scheme
Speaker : Anmoal Porwal - Technical University of Munich
I will present our key-recovery attack on the ACDGV public-key encryption scheme proposed at ASIACRYPT 2024 by Aragon, Couvreur, Dyseryn, Gaborit, and Vinçotte. The secret key is a Gabidulin code hidden by appending random rows and columns and by left- and right-multiplication with invertible matrices. Our attack exploits the resulting algebraic structure to recover an equivalent secret key. It[…]-
Cryptography
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Asymmetric primitive
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Module Learning With Errors and Structured Extrapolated Dihedral Cosets
Speaker : Jinwei Zheng - Télécom Paris
The Module Learning With Errors (MLWE) problem is the fundamental hardness assumption underlying the key encapsulation and signature schemes ML-KEM and ML-DSA, which have been selected by NIST for post-quantum cryptography standardization. Understanding its quantum hardness is crucial for assessing the security of these standardized schemes. Inspired by the equivalence between LWE and[…]-
Cryptography
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