Learning With Differential Privacy.

AbstractConsidering the internal representations in machine learning models may potentially imply some of training data, an adversary may launch attacks to extract parts of training data from a trained model (e.g., the “black-box” model-inversion attack Fredrikson et al. (2015)). Thus, the privacy guarantees if datasets contain correlated inputs is also important.