Paper 2024/966

Diffuse Some Noise: Diffusion Models for Measurement Noise Removal in Side-channel Analysis

Abstract

Resilience against side-channel attacks is an important consideration for cryptographic implementations deployed in devices with physical access to the device. However, noise in side-channel measurements has a significant impact on the complexity of these attacks, especially when an implementation is protected with masking. Therefore, it is important to assess the ability of an attacker to deal with noise. While some previous works have considered approaches to remove (some) noise from measurements, these approaches generally require considerable expertise to be effectively employed or necessitate the ability of the attacker to capture a 'clean' set of traces without the noise. In this paper, we introduce a method for utilizing diffusion models to remove measurement noise from side-channel traces in a fully non-profiled setting. Denoising traces using our method considerably lowers the complexity of mounting attacks in both profiled and non-profiled settings. For instance, for a collision attack against the ASCADv2 dataset, we reduced the number of traces required to retrieve the key by 40%, and we showed similar improvements for ESHARD using a state-of-the-art MORE attack. Furthermore, we provide analyses into the scenarios where our method is useful and generate insights into how the diffusion networks denoise traces.