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Privacy Preserving

Papers

Showing 76100 of 2975 papers

TitleStatusHype
Adversarially-Guided Portrait MattingCode1
DistFL: Distribution-aware Federated Learning for Mobile ScenariosCode1
Domain-Invariant Representation Learning from EEG with Private EncodersCode1
DP-MERF: Differentially Private Mean Embeddings with Random Features for Practical Privacy-Preserving Data GenerationCode1
Defending against Backdoors in Federated Learning with Robust Learning RateCode1
A Survey for Federated Learning Evaluations: Goals and MeasuresCode1
Differentially Private Optimization on Large Model at Small CostCode1
ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram DigitizationCode1
Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial IntelligenceCode1
Differentially Private Bias-Term Fine-tuning of Foundation ModelsCode1
Differentially Private Federated Learning: A Client Level PerspectiveCode1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret SharingCode1
A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and CountermeasuresCode1
A2XP: Towards Private Domain GeneralizationCode1
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
Exploring Federated Unlearning: Review, Comparison, and InsightsCode1
DeceFL: A Principled Decentralized Federated Learning FrameworkCode1
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and ApplicationsCode1
A Survey on Vulnerability of Federated Learning: A Learning Algorithm PerspectiveCode1
DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt EngineerCode1
APPFL: Open-Source Software Framework for Privacy-Preserving Federated LearningCode1
APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a ServiceCode1
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and ReleaseCode1
CTAB-GAN+: Enhancing Tabular Data SynthesisCode1
Antipodes of Label Differential Privacy: PATE and ALIBICode1
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