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I add differential privacy (DP) to my machine learning models by using PyTorch-DP. PyTorch-DP supplies me with the values: $\epsilon$ and $\delta $. I know that the $\epsilon$ tells us something about the probability $\rho$ that the privacy of individuals in the dataset (DP) is broken. So, a $\rho$ of 0.5 might correspond to an $\epsilon$ of 2 (depending on the statistics that are released).

So now, my question is: With the implementation that PyTorch-DP uses (Renyi DP calculated by using the Moments Accountant method), to what $\rho$ does the $\epsilon$ correspond to? If I do not know this, how can I say something "meaningful" about the level of privacy $\rho$ that $\epsilon$ satisfies?

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  • $\begingroup$ This is cross-posted on Information Security site. Please if you are the same person, delete one copy. $\endgroup$ – kelalaka Jun 24 '20 at 14:25
  • $\begingroup$ I don't think your interpretation of $\varepsilon$ is quite right. What do you mean by "the probability $\rho$ that the privacy of individuals in the dataset is broken"? I generally find the Bayesian interpretation of $\varepsilon$ to be the most accessible. I also don't understand how this question is about Rényi privacy, whose parameters are not $(\varepsilon,\delta)$ but $(\alpha,\varepsilon)$. Can you clarify? $\endgroup$ – Ted Sep 25 '20 at 12:17

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