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Privacy Preserving Deep Learning

The goal of privacy-preserving (deep) learning is to train a model while preserving privacy of the training dataset. Typically, it is understood that the trained model should be privacy-preserving (e.g., due to the training algorithm being differentially private).

Papers

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Showing 51–59 of 59 papers

TitleStatusHype
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud—0
A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component—0
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New DatasetCode1
Private Deep Learning with Teacher Ensembles—0
Towards Fair and Privacy-Preserving Federated Deep ModelsCode0
Distributed Layer-Partitioned Training for Privacy-Preserved Deep Learning—0
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning—0
A generic framework for privacy preserving deep learningCode0
Privacy-Preserving Deep Learning via Weight Transmission—0
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