Duality Between Smooth Min- and Max-Entropies

TL;DR

This paper extends a duality relation between smooth min- and max-entropies in information theory, revealing their fundamental connection and implications for cryptography and data processing.

Contribution

It generalizes the duality between min- and max-entropies to the smooth case, linking operational quantities in classical and quantum information theory.

Findings

Smooth min-entropy equals negative smooth max-entropy conditioned on a purifying system.

Operational quantities like data compression and randomness extraction are related through this duality.

Results have potential applications in cryptographic security proofs.

Abstract

In classical and quantum information theory, operational quantities such as the amount of randomness that can be extracted from a given source or the amount of space needed to store given data are normally characterized by one of two entropy measures, called smooth min-entropy and smooth max-entropy, respectively. While both entropies are equal to the von Neumann entropy in certain special cases (e.g., asymptotically, for many independent repetitions of the given data), their values can differ arbitrarily in the general case. In this work, a recently discovered duality relation between (non-smooth) min- and max-entropies is extended to the smooth case. More precisely, it is shown that the smooth min-entropy of a system A conditioned on a system B equals the negative of the smooth max-entropy of A conditioned on a purifying system C. This result immediately implies that certain operational quantities (such as the amount of compression and the amount of randomness that can be extracted from given data) are related. Such relations may, for example, have applications in cryptographic security proofs.