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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 1–25 of 59 papers

TitleStatusHype
Fawkes: Protecting Privacy against Unauthorized Deep Learning ModelsCode3
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic DataCode2
DCT-CryptoNets: Scaling Private Inference in the Frequency DomainCode1
Split Without a Leak: Reducing Privacy Leakage in Split LearningCode1
Antipodes of Label Differential Privacy: PATE and ALIBICode1
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUCode1
Tempered Sigmoid Activations for Deep Learning with Differential PrivacyCode1
Locally Private Graph Neural NetworksCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New DatasetCode1
A Training Framework for Optimal and Stable Training of Polynomial Neural NetworksCode0
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation—0
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability—0
Just a Simple Transformation is Enough for Data Protection in Vertical Federated LearningCode0
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation—0
Low-Latency Privacy-Preserving Deep Learning Design via Secure MPC—0
Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task LearningCode0
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption—0
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning—0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
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
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