Machine-learning-aided side-channel analysis in post-quantum cryptography
Title: Machine-learning-aided side-channel analysis in post-quantum cryptography
DNr: Berzelius-2024-53
Project Type: LiU Berzelius
Principal Investigator: Qian Guo <>
Affiliation: Lunds universitet
Duration: 2024-02-12 – 2024-09-01
Classification: 10201


In the realm of cryptographic research, post-quantum cryptography has emerged as a central focus. The rapid strides in quantum computing technology have prompted the National Institute of Standards and Technology (NIST) to embark on a crucial mission: the Post-Quantum Cryptography Standardization Project. This initiative aims to identify robust replacements for our current public-key encryption and signature standards, which face imminent threats from quantum computers. As the project nears its conclusion, NIST is poised to unveil a new internet standard. Our research endeavors delve into the security of the selected cryptographic schemes, particularly when side-channel leakage is taken into account. The implications of our work extend far beyond theoretical realms; they directly impact the cryptographic techniques we rely on daily. As post-quantum cryptography becomes more widely deployed, our efforts contribute to securing sensitive information against potential cyberattacks from quantum computers