Machine Learning at Scale with Differential Privacy in TensorFlow

Monday, August 12, 2019 - 2:30 pm3:00 pm

Nicolas Papernot, Google Brain


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.

Nicolas Papernot, Google Brain

Nicolas Papernot is a research scientist at Google Brain working on the security and privacy of machine learning. He will join the University of Toronto and Vector Institute as an assistant professor and Canada CIFAR AI Chair in the Fall 2019. He earned his Ph.D. in Computer Science and Engineering at the Pennsylvania State University, working with Prof. Patrick McDaniel and supported by a Google PhD Fellowship in Security and Privacy. Nicolas received a best paper award at ICLR 2017. He is also the co-author of CleverHans, an open-source library widely adopted in the technical community to benchmark machine learning in adversarial settings, and tf.Privacy, an open-source library for training differentially private models with TensorFlow. He serves on the program committees of several conferences including ACM CCS, IEEE S&P, and USENIX Security. In 2016, he received his M.S. in Computer Science and Engineering from the Pennsylvania State University and his M.S. in Engineering Sciences from the Ecole Centrale de Lyon.

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@inproceedings {238162,
author = {Nicolas Papernot},
title = {Machine Learning at Scale with Differential Privacy in {TensorFlow}},
booktitle = {2019 {USENIX} Conference on Privacy Engineering Practice and Respect ({PEPR} 19)},
year = {2019},
address = {Santa Clara, CA},
url = {},
publisher = {USENIX Association},
month = aug