A statistical model for DPA when algorithmic noise is dependent on target

Abstract

In side‐channel attacks, information about any intermediate data except the target data will be considered as noise, namely, the “algorithmic noise.” Algorithmic noise is always assumed to be independent of the target data. As a result, it is thought to be an equivalence of physical noise. Numerous investigations are built on this intuitive assumption. In this paper, we claim that this assumption is not always true and find the algorithm (e.g., SIMON) for which the algorithmic noise is dependent on the target data. On this condition, we reconsider the issue of success rate estimation and build a modified statistical model to accurately estimate the success rate when the algorithmic noise is dependent on the target data. Finally, experimental results for SIMON algorithm validate the soundness of our methodology. It is also worth mentioning that SIMON is a candidate proposed by the National Security Agency as the standard light‐weight block cipher. Copyright © 2016 John Wiley & Sons, Ltd.