ML-Based Security Analysis and Mitigation of Homomorphic Encryption Side-Channels, CAEML Core Project 5A4
Paul D. Franzon
Project runs from 01/01/2021 to 07/31/2022
Power-based side-channel attacks are a fundamental threat for cryptographic systems because they can extract secret-key information from its intrinsic correlation to the power consumption of the underlying device. This research analyzes the vulnerability of next-generation cryptographic systems, called homomorphic encryption, against power-based side-channel attacks and explores the efficiency of the state-of-the-art machine-learning (ML) classifiers to carry out the attack.