Scatter: a Missing Case?

Zhou, Yuanyuan [UCL] Duval, Sébastien [UCL] Standaert, François-Xavier [UCL]

Scatter is a multivariate transform proposed in combination with the Chi2 and MIA distinguishers at COSADE 2018. Its primary motivation is to inherently deal with the misalignment and synchronization issues that may decrease the efficiency of concrete side-channel attacks. In this paper, we first show empirically that when compared to natural competitors for first-order multivariate attacks (e.g., exploiting linear regression on-the-y), it does not bring improvements in the (simulated and actual) implementation settings studied by its authors. We then show that the same holds in the higher-order case: in most practically relevant settings, Scatter works best when combined with a combination function mixing the leakage samples in a non-linear manner, bringing it back to a situation where it does not improve standard distinguishers.