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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
Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach—0
Communication-Efficient Federated Distillation with Active Data Sampling—0
Backpropagation Clipping for Deep Learning with Differential PrivacyCode0
DP-FP: Differentially Private Forward Propagation for Large Models—0
SoK: Privacy-preserving Deep Learning with Homomorphic Encryption—0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Towards Secure and Practical Machine Learning via Secret Sharing and Random PermutationCode0
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep LearningCode0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services—0
Variational Leakage: The Role of Information Complexity in Privacy LeakageCode0
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