Asymptotically Optimal Massey-Like Inequality on Guessing Entropy With Application to Side-Channel Attack Evaluations
This work provides enhanced theoretical tools for evaluating side-channel attacks, which is crucial for cybersecurity, though it is incremental as it refines prior inequalities.
The paper derived an asymptotically optimal lower bound on guessing entropy using Shannon entropy, refining it for finite-support distributions, and demonstrated its application to side-channel attack evaluations, showing improvements over existing bounds.
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.