International Association
for Cryptologic Research

Template attacks and stochastic models are among the most powerful

side-channel attacks. However, they can be computationally expensive

when processing a large number of samples. Various compression

techniques have been used very successfully to reduce the data

dimensionality prior to applying template attacks, most notably

Principal Component Analysis (PCA) and Fisher\'s Linear Discriminant

Analysis (LDA). These make the attacks more efficient computationally

and help the profiling phase to converge faster. We show how these

ideas can also be applied to implement stochastic models more

efficiently, and we also show that they can be applied and evaluated

even for more than eight unknown data bits at once.