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.

翻译：类似Massey的不平等对于从可计算缩放的香农 entropy 角度来测测测诱变值是任何有用的下限。 确定并进一步完善了无症状的最佳Massey式的不平等,用于有限支持分布。 这些结果的影响在侧道攻击评估中被突出,其中假设诱变是一个关键指标。 在这方面,获得的界限与最新技术进行比较。