Curry On
June 19-20th, 2017

Machine learning systems with privacy and for privacy: TensorFlow & PATE-G
Martín Abadi


Machine learning is powered by training data. In this talk, we discuss the privacy of training data and how to protect it. We describe one recent technique for this purpose, PATE-G, where several models based on different subsets of the training data are combined into one model that does not depend “too much” on any particular piece of the training data.

Machine learning is enabled by software systems. These systems should efficiently support both established techniques (e.g., stochastic gradient descent) and newer ones (e.g., adversarial networks). In this talk, we focus on TensorFlow, a flexible, programmable system for large-scale machine learning.

TensorFlow and PATE-G go well together. In particular, PATE-G is not tied to one particular learning algorithm. Conversely, TensorFlow makes it easy to express the techniques on which PATE-G relies.

The talk is based on joint work with many people, primarily in Google Brain. More information on TensorFlow can be found at PATE-G is described in a paper available at


Martín Abadi is a Principal Scientist at Google, in the Google Brain team. He is also a Professor Emeritus at the University of California at Santa Cruz, where was a Professor in the Computer Science Department till 2013. He has held an annual Chair at the Collège de France, has taught at Stanford University and the University of California at Berkeley, and has worked at Digital’s System Research Center, Microsoft Research Silicon Valley, and other industrial research labs. He received his Ph.D. at Stanford University in 1987. His research is mainly on computer and network security, programming languages, and specification and verification methods. It has been recognized with the Outstanding Innovation Award of the ACM Special Interest Group on Security, Audit and Control and with the Hall of Fame Award of the ACM Special Interest Group on Operating Systems, among other awards. He is a Fellow of the Association for Computing Machinery and of the American Association for the Advancement of Science (AAAS). He holds a doctorate honoris causa from École normale supérieure de Cachan.