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Approximate and Probabilistic Differential Privacy Definitions
Authors: Sebastian MeiserAbstract:
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.
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