Random projections in a distributed environment for privacy-preserved deep learning

Random projections in a distributed environment for privacy-preserved deep learning

The field of Deep Learning (DL) only over the last decade has proven useful for increasingly more complex Machine Learning tasks and data, a notable milestone being generative models achieving facial synthesis indistinguishable from real faces. With the increased complexity in DL architecture and tr...

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Bibliographic Details
Main Author: Bagger Toräng, Malcolm
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2021
Subjects:
Random projections
Generative adversarial networks
Privacy metrics
Deep learning
Obfuscation.
Slumpmässiga projektioner
Generativa kontroversiella nätverk
Privatiserings-mått
Djupinlärning
Obfuskering.
Computer Sciences
Datavetenskap (datalogi)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-305817

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-305817

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