SOTAVerified

Privacy Preserving

Papers

Showing 21012125 of 2975 papers

TitleStatusHype
Masked LARk: Masked Learning, Aggregation and Reporting worKflowCode1
Locally Differentially Private Bayesian Inference0
FedPrune: Towards Inclusive Federated Learning0
DPCOVID: Privacy-Preserving Federated Covid-19 Detection0
MarS-FL: Enabling Competitors to Collaborate in Federated Learning0
Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization CollaborationsCode0
EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting0
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy0
Tackling the Local Bias in Federated Graph Learning0
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
ML with HE: Privacy Preserving Machine Learning Inferences for Genome StudiesCode0
SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree0
Privacy in Open Search: A Review of Challenges and Solutions0
Statistical and Topological Properties of Gaussian Smoothed Sliced Probability Divergences0
Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching0
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing0
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes0
A Federated Approach to Predict Emojis in Hindi Tweets0
Sparse Distillation: Speeding Up Text Classification by Using Bigger Student ModelsCode1
Noise-Augmented Privacy-Preserving Empirical Risk Minimization with Dual-purpose Regularizer and Privacy Budget Retrieval and Recycling0
Blockchain and Federated Edge Learning for Privacy-Preserving Mobile Crowdsensing0
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture0
Efficient privacy-preserving inference for convolutional neural networks0
Occupancy Estimation from Thermal Images0
FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning0
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