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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 2130 of 59 papers

TitleStatusHype
Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep LearningCode0
Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)Code0
Backpropagation Clipping for Deep Learning with Differential PrivacyCode0
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and UtilityCode0
Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task LearningCode0
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imagingCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Secure Data Sharing With Flow ModelCode0
Just a Simple Transformation is Enough for Data Protection in Vertical Federated LearningCode0
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