Scatter: a Missing Case?

Scatter is a multivariate transform proposed in combination with the Chi\(^2\) 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-fly), 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.