SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 31–40 of 59 papers

TitleStatusHype
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep LearningCode0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services—0
Antipodes of Label Differential Privacy: PATE and ALIBICode1
Variational Leakage: The Role of Information Complexity in Privacy LeakageCode0
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPUCode1
Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep LearningCode0
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models—0
Can we Generalize and Distribute Private Representation Learning?Code0
Secure Data Sharing With Flow ModelCode0
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning—0
Show:102550
← PrevPage 4 of 6Next →

No leaderboard results yet.