Approximate and Probabilistic Differential Privacy Definitions

Sebastian Meiser

Abstract: This technical report discusses three subtleties related to the widely used notion of differential privacy (DP). First, we discuss how the choice of a distinguisher influences the privacy notion and why we should always have a distinguisher if we consider approximate DP. Secondly, we draw a line between the very intuitive probabilistic differential privacy (with probability $1-\delta$ we have $\varepsilon$-DP) and the commonly used approximate differential privacy ($(\varepsilon,\delta)$-DP). Finally we see that and why probabilistic differential privacy (and similar notions) are not complete under post-processing, which has significant implications for notions used in the literature.