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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 31–40 of 59 papers

TitleStatusHype
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks—0
Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator—0
Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach—0
Security and Privacy Preserving Deep Learning—0
SoK: Privacy-preserving Deep Learning with Homomorphic Encryption—0
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability—0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services—0
Training Differentially Private Graph Neural Networks with Random Walk Sampling—0
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning—0
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
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