Welcome to the resource topic for 2018/277
Title:
Approximate and Probabilistic Differential Privacy Definitions
Authors: 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.
ePrint: https://eprint.iacr.org/2018/277
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .