Asymptotically Optimal Massey-Like Inequality on Guessing Entropy With Application to Side-Channel Attack Evaluations

TL;DR

This paper derives an asymptotically optimal Massey-like inequality providing tighter lower bounds on guessing entropy from Shannon entropy, with applications to evaluating side-channel attack security.

Contribution

It introduces the asymptotically optimal Massey-like inequality and refines it for finite-support distributions, improving side-channel attack assessments.

Findings

Derived the asymptotically optimal Massey-like inequality.

Refined bounds for finite-support distributions.

Compared new bounds to existing state-of-the-art methods.

Abstract

A Massey-like inequality is any useful lower bound on guessing entropy in terms of the computationally scalable Shannon entropy. The asymptotically optimal Massey-like inequality is determined and further refined for finite-support distributions. The impact of these results are highlighted for side-channel attack evaluation where guessing entropy is a key metric. In this context, the obtained bounds are compared to the state of the art.