Keynote Session 4: The Shuffle Model and Federated Learning, by Ilya Mironov (Facebook)
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Workshop: Federated Learning for User Privacy and Data Confidentiality
Abstract
Abstract: The shuffle model of computation, also known as the Encode-Shuffle-Analyze (ESA) architecture, is a recently introduced powerful approach towards combining anonymization channels and differentially private distributed computations. We present general results about amplification-by-shuffling unlocked by ESA, as well as more specialized theoretical and empirical findings. We discuss challenges of instantiating the shuffle model in practice.
Biography: Ilya Mironov obtained his Ph.D. in cryptography from Stanford in 2003. In 2003-2014 he was a member of Microsoft Research-Silicon Valley Campus, where he contributed to early works on differential privacy. In 2015-2019 he worked in Google Brain. Since 2019 he has been part of Facebook AI working on privacy-preserving machine learning.