Second-Order Expansion of Privacy Amplification Under f-Divergence Criteria

Abstract

We derive the second-order asymptotics of randomness extraction from memoryless sources with side information under security criteria based on a broad class of Csisz\`ar f-divergences, treating both a fixed reference side-information marginal and optimization over that marginal. The conditional varentropy decomposes into fluctuations of the conditional entropy across different values of the side information and the average variance of the conditional surprisal for each value. Without marginal optimization, these contributions yield a Gaussian-mixture second-order profile. With marginal optimization, they combine into the total conditional varentropy, yielding a single Gaussian profile. As corollaries, we obtain second-order expansions for R\'enyi-entropy criteria of all orders $\alpha \in (0,1)$ and recover the known expansion for total variation distance.