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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 21–30 of 59 papers

TitleStatusHype
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning—0
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imagingCode0
Training Differentially Private Graph Neural Networks with Random Walk Sampling—0
Memorization of Named Entities in Fine-tuned BERT ModelsCode0
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)Code0
Privacy-preserving Deep Learning based Record Linkage—0
Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator—0
Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection—0
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and UtilityCode0
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