Title:	Machine-learning-aided side-channel analysis in post-quantum cryptography
DNr:	Berzelius-2024-295
Project Type:	LiU Berzelius
Principal Investigator:	Qian Guo <qian.guo@eit.lth.se>
Affiliation:	Lunds universitet
Duration:	2024-09-01 – 2025-03-01
Classification:	10201
Keywords:

Abstract

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. In the previous project period, we have made significant progress in this area, identifying two promising models for attacking protected hardware and software implementations. We aim to further evaluate the crypto applications and implications of these models, with the goal of publishing two high-quality papers that contribute valuable insights to the field. 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.