In order to maintain a similar security level in a post-quantum setting, many symmetric primitives should have to double their keys and increase their state sizes. So far, no generic way for doing this is known that would provide convincing quantum security guarantees. In this paper we propose a new generic construction, QuEME, that allows one to double the key and the state size of a block cipher in such a way that a decent level of quantum security is guaranteed. The QuEME design is inspired by the ECB-Mix-ECB (EME) construction, but is defined for a different choice of mixing function than what we have seen before, in order to withstand a new quantum superposition attack that we introduce as a side result: this quantum superposition attack exhibits a periodic property found in collisions and breaks EME and a large class of its variants. We prove that QuEME achieves n-bit security in the classical setting, where n is the block size of the underlying block cipher, and at least (n/6)-bit security in the quantum setting. We finally propose a concrete instantiation of this construction, called Double-AES, that is built with variants of the standardized AES-128 block cipher.

The Crossbred algorithm is currently the state-of-the-art method for solving overdetermined multivariate polynomial systems over $\mathbb{F}_2$. Since its publication in 2017, several record breaking implementations have been proposed and demonstrate the power of this hybrid approach. Despite these practical results, the complexity of this algorithm and the choice of optimal parameters for it are difficult open questions. In this paper, we prove a bivariate generating series for potentially admissible parameters of the Crossbred algorithm.

Recently, Geraud-Stewart and Naccache proposed two trapdoors based on matrix products. In this paper, we answer the call for cryptanalysis. We explore how using the trace and determinant of a matrix can be used to attack their constructions. We fully break their first construction in a polynomial-time attack. We show an information leak in the second construction using characteristic polynomials, and provide two attacks that decrease the bit security by about half.

For more than two decades, pairings have been a fundamental tool for designing elegant cryptosystems, varying from digital signature schemes to more complex privacy-preserving constructions. However, the advancement of quantum computing threatens to undermine public-key cryptography. Concretely, it is widely accepted that a future large-scale quantum computer would be capable to break any public-key cryptosystem used today, rendering today's public-key cryptography obsolete and mandating the transition to quantum-safe cryptographic solutions. This necessity is enforced by numerous recognized government bodies around the world, including NIST which initiated the first open competition in standardizing post-quantum (PQ) cryptographic schemes, focusing primarily on digital signatures and key encapsulation/public-key encryption schemes. Despite the current efforts in standardizing PQ primitives, the landscape of complex, privacy-preserving cryptographic protocols, e.g., zkSNARKs/zkSTARKs, is at an early stage. Existing solutions suffer from various disadvantages in terms of efficiency and compactness and in addition, they need to undergo the required scrutiny to gain the necessary trust in the academic and industrial domains. Therefore, it is believed that the migration to purely quantum-safe cryptography would require an intermediate step where current classically secure protocols and quantum-safe solutions will co-exist. This is enforced by the report of the Commercial National Security Algorithm Suite version 2.0, mandating transition to quantum-safe cryptographic algorithms by 2033 and suggesting to incorporate ECC at 192-bit security in the meantime. To this end, the present paper aims at providing a comprehensive study on pairings at 192-bit security level. We start with an exhaustive review in the literature to search for all possible recommendations of such pairing constructions, from which we extract the most promising candidates in terms of efficiency and security, with respect to the advanced Special TNFS attacks. Our analysis is focused, not only on the pairing computation itself, but on additional operations that are relevant in pairing-based applications, such as hashing to pairing groups, cofactor clearing and subgroup membership testing. We implement all functionalities of the most promising candidates within the RELIC cryptographic toolkit in order to identify the most efficient pairing implementation at 192-bit security and provide extensive experimental results.

Profiling side-channel analysis has gained widespread acceptance in both academic and industrial realms due to its robust capacity to unveil protected secrets, even in the presence of countermeasures. To harness this capability, an adversary must access a clone of the target device to acquire profiling measurements, labeling them with leakage models. The challenge of finding an effective leakage model, especially for a protected dataset with a low signal-to-noise ratio or weak correlation between actual leakages and labels, often necessitates an intuitive engineering approach, as otherwise, the attack will not perform well.

In this paper, we introduce a deep learning approach with a flexible leakage model, referred to as the multi-bit model. Instead of trying to learn a pre-determined representation of the target intermediate data, we utilize the concept of the stochastic model to decompose the label into bits. Then, the deep learning model is used to classify each bit independently. This versatile multi-bit model can adjust to existing leakage models like the Hamming weight and Most Significant Bit while also possessing the flexibility to adapt to complex leakage scenarios. To further improve the attack efficiency, we extend the multi-bit model to profile all 16 subkey bytes simultaneously, which requires negligible computational effort. The experimental results show that the proposed methods can efficiently break all key bytes across four considered datasets while the conventional leakage models fail. Our work signifies a significant step forward in deep learning-based side-channel attacks, showcasing a high degree of flexibility and efficiency with the proposed leakage model.

We prove that isogenies between Drinfeld F[x]-modules over a finite field can be computed in polynomial time. This breaks Drinfeld analogs of isogeny-based cryptosystems.