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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
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
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
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning—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
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