With the rapid advancements in information technologies such as 5G and cloud computing,
Internet of Things (IoT) applications are expanding across various domains, such as smart …

At CRYPTO'19, Gohr proposed a new cryptanalysis strategy based on the utilisation of
machine learning algorithms. Using deep neural networks, he managed to build a neural …

At CRYPTO'19, Gohr first introduces the deep learning-based cryptanalysis on round-
reduced SPECK. Using a deep residual network, Gohr trains several neural network-based …

Currently deployed public-key cryptosystems will be vulnerable to attacks by full-scale
quantum computers. Consequently," quantum resistant" cryptosystems are in high demand …

Most of the traditional cryptanalytic technologies often require a great amount of time, known
plaintexts, and memory. This paper proposes a generic cryptanalysis model based on deep …

Learning with Errors (LWE) is a hard math problem used in post-quantum cryptography.
Homomorphic Encryption (HE) schemes rely on the hardness of the LWE problem for their …

Secure multi-party computation (MPC) has recently become prominent as a concept to
enable multiple parties to perform privacy-preserving machine learning without leaking …

Neural-aided cryptanalysis is a challenging topic, in which the neural distinguisher () is a
core module. In this paper, we propose a new considering multiple ciphertext pairs …

Since the introduction of differential-neural cryptanalysis, as the machine learning assisted
differential cryptanalysis proposed in [Goh19] is coined by now, a lot of followup works have …

In CRYPTO 2019, Gohr showed that well-trained neural networks could perform
cryptanalytic distinguishing tasks superior to differential distribution table (DDT)-based …