Comparative Evaluation of Rank Correlation Based DPA on an AES Prototype Chip

We propose a new class of distinguishers for differential sidechannel analysis based on nonparametric statistics. As an example we use Spearman’s rank correlation coefficient. We present a comparative study of several statistical methods applied to real power measurements from an AES prototype chip to demonstrate the effectiveness of the proposed method. Our study shows that Spearman’s rank coefficient outperforms all other univariate tests under consideration. In particular we note that Pearson’s correlation coefficient requires about three times more samples for reliable key recovery than the method we propose. Further, multivariate methods with a profiling step which are commonly assumed to be the most powerful attacks are not significantly more efficient at key extraction than the attack we propose. Our results indicate that power models which are linear in the transition count are not optimal for the attacked prototype chip.