University of California, Santa Cruz
Template attacks remain a powerful side-channel technique to eavesdrop on tamper-resistant hardware. They model the probability distribution of leaking signals and noise to guide a search for secret data values. In practice, several numerical obstacles can arise when implementing such attacks with multivariate normal distributions. The authors propose efficient methods to avoid these. They also demonstrate how to achieve significant performance improvements, both in terms of information extracted and computational cost, by pooling covariance estimates across all data values.