Paper 2018/550

PRank: Fast Analytical Rank Estimation via Pareto Distributions

Liron David and Avishai Wool

Abstract

Rank estimation is an important tool for a side-channel evaluations laboratories. It allows estimating the remaining security after an attack has been performed, quantified as the time complexity and the memory consumption required to brute force the key given the leakages as probability distributions over $d$ subkeys (usually key bytes). These estimations are particularly useful where the key is not reachable with exhaustive search. We propose a new method called PRank for rank estimation, that is conceptually simple, and more time and memory efficient than previous proposals. Our main idea is to bound each subkey distribution by a Pareto-like function: since these are analytical functions, we can then estimate the rank by a closed formula. We evaluated the performance of PRank through extensive simulations based on two real SCA data corpora, and compared it to the currently-best histogram-based algorithm. We show that PRank gives a good rank estimation with much improved time and memory efficiency, especially for large ranks: For ranks between $2^{80}-2^{100}$ PRank estimation is at most 10 bits above the histogram rank and for ranks beyond $2^{100}$ the PRank estimation is only 4 bits above the histogram rank---yet it runs faster, and uses negligible memory. PRank gives a new and interesting method to solve the rank estimation problem based on reduction to analytical functions and calculating one closed formula hence using negligible time and space.