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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 21–30 of 59 papers

TitleStatusHype
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
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