Before presenting our algorithm we provide bounds on the guessing entropy of the full key in terms of the easy-to-compute guessing entropies of the individual subkeys. We use these results to quantify the near-optimality of our algorithm's ranking, and to bound its guessing entropy. We evaluated our algorithm through extensive simulations. We show that our algorithm continues its near-optimal-order enumeration far beyond the rank at which the optimal algorithm fails due to insufficient memory, on realistic SCA scenarios. Our simulations utilize a new model of the true rank distribution, based on long tail Pareto distributions, that is validated by empirical data and may be of independent interest.
Category / Keywords: Date: received 28 Dec 2015 Contact author: yash at eng tau ac il, lirondavid@gmail com Available format(s): PDF | BibTeX Citation Version: 20151228:162940 (All versions of this report) Short URL: ia.cr/2015/1236 Discussion forum: Show discussion | Start new discussion