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Data holders are increasingly seeking to protect their user's privacy, whilststill maximizing their ability to produce machine models with high qualitypredictions. In this work, we empirically evaluate various implementations ofdifferential privacy (DP), and measure their ability to fend off real-worldprivacy attacks, in addition to measuring their core goal of providing accurateclassifications. We establish an evaluation framework to ensure each of theseimplementations are fairly evaluated. Our selection of DP implementations addDP noise at different positions within the framework, either at the point ofdata collection/release, during updates while training of the model, or aftertraining by perturbing learned model parameters. We evaluate eachimplementation across a range of privacy budgets, and datasets, eachimplementation providing the same mathematical privacy guarantees. By measuringthe models' resistance to real world attacks of membership and attributeinference, and their classification accuracy. we determine whichimplementations provide the most desirable tradeoff between privacy andutility. We found that the number of classes of a given dataset is unlikely toinfluence where the privacy and utility tradeoff occurs. Additionally, in thescenario that high privacy constraints are required, perturbing input trainingdata does not trade off as much utility, as compared to noise added later inthe ML process.

Not one but many Tradeoffs: Privacy Vs. Utility in Differentially Private Machine Learning