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Privacy-Preserving Deep Learning : A Comprehensive Survey by Kim, Kwangjo & Tanuwidjaja, Harry Chandra is a nonfiction available to read on EtoBox.

What is Privacy-Preserving Deep Learning : A Comprehensive Survey about?

This book discusses the state-of-the-art in privacy-preserving deep learning (PPDL), especially  as a tool for machine learning as a service (MLaaS), which serves as an enabling technology by combining classical privacy-preserving and cryptographic protocols with deep learning. Google and Microsoft announced a major investment in PPDL in early 2019. This was followed by Google’s infamous announcement of “Private Join and Compute,” an open source PPDL tools based on secure multi-party computation (secure MPC) and homomorphic encryption (HE) in June of that year. One of the challenging issues concerning PPDL is selecting its practical applicability despite the gap between the theory and practice. In order to solve this problem, it has recently been proposed that in addition to classical privacy-preserving methods (HE, secure MPC, differential privacy, secure enclaves), new federated or split learning for PPDL should also be applied. This concept involves building a cloud framework that enables collaborative learning while keeping training data on client devices. This successfully preserves privacy and while allowing the framework to be implemented in the real world.    This book prov

Who reads Privacy-Preserving Deep Learning : A Comprehensive Survey?

Author

Kim, Kwangjo & Tanuwidjaja, Harry Chandra

Publisher

Springer Singapore : Imprint: Springer

Published

2021

Language

EN

ISBN

9789811637636

Category

nonfiction

Subjects

Computer Science, Mathematics, Science