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Presented at PEPR'19 2019 by

Frank Kargl

Nicolas Papernot

This talk will illustrate how learning with rigorous differential privacy guarantees is possible using TensorFlow Privacy, an open-source library that makes it easier not only for developers to train ML models with privacy in real-world systems, but also for researchers to advance the state-of-the-art in ML with strong privacy guarantees.