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?
