Kunal Talwar (Google)
Monday, April 1, 2019
Klaus 1116E - 11:00 am
Title: Amplification Theorems for Differentially Private Machine Learning
Abstract: A rigorous foundational approach to private data analysis has emerged in theoretical computer science in the last decade, with differential privacy and its close variants playing a central role. We have recently been able to train complex machine learning models with little accuracy loss, while giving strong differentially privacy guarantees. The analyses of these algorithms rely on a class of results known as privacy amplification theorems. In this talk, I will sketch how private ML models can be trained, and how they can be analysed. I will then describe two recent privacy amplification theorems, and some of their implications.
(Joint works with Ulfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan and Abhradeep Thakurta)
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