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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 1–50 of 59 papers

TitleStatusHype
Fawkes: Protecting Privacy against Unauthorized Deep Learning ModelsCode3
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic DataCode2
Locally Private Graph Neural NetworksCode1
DCT-CryptoNets: Scaling Private Inference in the Frequency DomainCode1
Tempered Sigmoid Activations for Deep Learning with Differential PrivacyCode1
Split Without a Leak: Reducing Privacy Leakage in Split LearningCode1
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New DatasetCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
Antipodes of Label Differential Privacy: PATE and ALIBICode1
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUCode1
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning—0
A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component—0
Communication-Efficient Federated Distillation with Active Data Sampling—0
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption—0
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation—0
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning—0
Distributed Layer-Partitioned Training for Privacy-Preserved Deep Learning—0
DP-FP: Differentially Private Forward Propagation for Large Models—0
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning—0
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning—0
Low-Latency Privacy-Preserving Deep Learning Design via Secure MPC—0
MPC Protocol for G-module and its Application in Secure Compare and ReLU—0
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models—0
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud—0
Privacy-preserving Deep Learning based Record Linkage—0
Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms—0
Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection—0
Privacy-Preserving Deep Learning via Weight Transmission—0
Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation—0
Private Deep Learning with Teacher Ensembles—0
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks—0
Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator—0
Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach—0
Security and Privacy Preserving Deep Learning—0
SoK: Privacy-preserving Deep Learning with Homomorphic Encryption—0
Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability—0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services—0
Training Differentially Private Graph Neural Networks with Random Walk Sampling—0
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning—0
Collaborative Training of Medical Artificial Intelligence Models with non-uniform LabelsCode0
Backpropagation Clipping for Deep Learning with Differential PrivacyCode0
A Training Framework for Optimal and Stable Training of Polynomial Neural NetworksCode0
Homogeneous Learning: Self-Attention Decentralized Deep LearningCode0
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imagingCode0
Just a Simple Transformation is Enough for Data Protection in Vertical Federated LearningCode0
Locally Differentially Private (Contextual) Bandits LearningCode0
A generic framework for privacy preserving deep learningCode0
Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI ModelsCode0
Variational Leakage: The Role of Information Complexity in Privacy LeakageCode0
Secure Data Sharing With Flow ModelCode0
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