Differentially private (DP) machine learning allows us to train models on
private data while limiting data leakage. DP formalizes this data leakage
through a cryptographic game, where an adversary must predict if a model was
trained on a dataset D, or a dataset D’ that differs in just one example.If
observing the training algorithm does not meaningfully increase the adversary’s
odds of successfully guessing which dataset the model was trained on, then the
algorithm is said to be differentially private. Hence, the purpose of privacy
analysis is to upper bound the probability that any adversary could
successfully guess which dataset the model was trained on.In our paper, we
instantiate this hypothetical adversary in order to establish lower bounds on
the probability that this distinguishing game can be won. We use this adversary
to evaluate the importance of the adversary capabilities allowed in the privacy
analysis of DP training algorithms.For DP-SGD, the most common method for
training neural networks with differential privacy, our lower bounds are tight
and match the theoretical upper bound. This implies that in order to prove
better upper bounds, it will be necessary to make use of additional
assumptions. Fortunately, we find that our attacks are significantly weaker
when additional (realistic)restrictions are put in place on the adversary’s
capabilities.Thus, in the practical setting common to many real-world
deployments, there is a gap between our lower bounds and the upper bounds
provided by the analysis: differential privacy is conservative and adversaries
may not be able to leak as much information as suggested by the theoretical

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