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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

Showing 1120 of 59 papers

TitleStatusHype
Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)Code0
A Training Framework for Optimal and Stable Training of Polynomial Neural NetworksCode0
Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task LearningCode0
Backpropagation Clipping for Deep Learning with Differential PrivacyCode0
A generic framework for privacy preserving deep learningCode0
Locally Differentially Private (Contextual) Bandits LearningCode0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
Memorization of Named Entities in Fine-tuned BERT ModelsCode0
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and UtilityCode0
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