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Differential privacy (DP) is the prevailing technique for protecting userdata in machine learning models. However, deficits to this framework include alack of clarity for selecting the privacy budget $\epsilon$ and a lack ofquantification for the privacy leakage for a particular data row by aparticular trained model. We make progress toward these limitations and a newperspective by which to visualize DP results by studying a privacy metric thatquantifies the extent to which a model trained on a dataset using a DPmechanism is ``covered" by each of the distributions resulting from training onneighboring datasets. We connect this coverage metric to what has beenestablished in the literature and use it to rank the privacy of individualsamples from the training set in what we call a privacy profile. Weadditionally show that the privacy profile can be used to probe an observedtransition to indistinguishability that takes place in the neighboringdistributions as $\epsilon$ decreases, which we suggest is a tool that canenable the selection of $\epsilon$ by the ML practitioner wishing to make useof DP.

Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile