Creating from Noise: Trace Generations Using Diffusion Model for Side-Channel Attack

In side-channel analysis (SCA), the success of an attack is largely dependent on the dataset sizes and the number of instances in each class. The generation of synthetic traces can help to improve attacks like profiling attacks. However, manually creating synthetic traces from actual traces is arduous. Therefore, automating this process of creating artificial traces is much needed. Recently, diffusion models have gained much recognition after beating another generative model known as Generative Adversarial Networks (GANs) in creating realistic images. We explore the usage of diffusion models in the domain of SCA. We proposed frameworks for a known mask setting and unknown mask setting in which the diffusion models could be applied. Under a known mask setting, we show that the traces generated under the proposed framework preserved the original leakage. Next, we demonstrated that the artificially created profiling data in the unknown mask setting can reduce the required attack traces for a profiling attack. This suggests that the artificially created profiling data from the trained diffusion model contains useful leakages to be exploited.