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A private machine learning algorithm hides as much as possible about itstraining data while still preserving accuracy. In this work, we study whether anon-private learning algorithm can be made private by relying on aninstance-encoding mechanism that modifies the training inputs before feedingthem to a normal learner. We formalize both the notion of instance encoding andits privacy by providing two attack models. We first prove impossibilityresults for achieving a (stronger) model. Next, we demonstrate practicalattacks in the second (weaker) attack model on InstaHide, a recent proposal byHuang, Song, Li and Arora [ICML'20] that aims to use instance encoding forprivacy.

Is Private Learning Possible with Instance Encoding?