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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 26–50 of 59 papers

TitleStatusHype
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
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
Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep LearningCode0
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models—0
Can we Generalize and Distribute Private Representation Learning?Code0
Secure Data Sharing With Flow ModelCode0
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning—0
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning—0
MPC Protocol for G-module and its Application in Secure Compare and ReLU—0
Security and Privacy Preserving Deep Learning—0
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks—0
Locally Differentially Private (Contextual) Bandits LearningCode0
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