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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
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUCode1
DCT-CryptoNets: Scaling Private Inference in the Frequency DomainCode1
Antipodes of Label Differential Privacy: PATE and ALIBICode1
Tempered Sigmoid Activations for Deep Learning with Differential PrivacyCode1
Split Without a Leak: Reducing Privacy Leakage in Split LearningCode1
Locally Private Graph Neural NetworksCode1
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New DatasetCode1
Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection—0
Privacy-preserving Deep Learning based Record Linkage—0
Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms—0
Disguised-Nets: Image Disguising for Privacy-preserving Outsourced Deep Learning—0
Privacy-Preserving Deep Learning via Weight Transmission—0
A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component—0
Generative Model-Based Attack on Learnable Image Encryption for Privacy-Preserving Deep Learning—0
Communication-Efficient Federated Distillation with Active Data Sampling—0
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models—0
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning—0
Converting Transformers to Polynomial Form for Secure Inference Over Homomorphic Encryption—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
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation—0
DP-FP: Differentially Private Forward Propagation for Large Models—0
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