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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 11–20 of 59 papers

TitleStatusHype
MPC Protocol for G-module and its Application in Secure Compare and ReLU—0
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation—0
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models—0
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning—0
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption—0
Communication-Efficient Federated Distillation with Active Data Sampling—0
A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component—0
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning—0
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning—0
DP-FP: Differentially Private Forward Propagation for Large Models—0
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