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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 41–50 of 59 papers

TitleStatusHype
Tempered Sigmoid Activations for Deep Learning with Differential PrivacyCode1
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning—0
MPC Protocol for G-module and its Application in Secure Compare and ReLU—0
Security and Privacy Preserving Deep Learning—0
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks—0
Locally Private Graph Neural NetworksCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
Locally Differentially Private (Contextual) Bandits LearningCode0
Fawkes: Protecting Privacy against Unauthorized Deep Learning ModelsCode3
Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms—0
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