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Privacy Preserving

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Showing 1551–1600 of 2975 papers

TitleStatusHype
Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health RecordsCode0
Vertical Federated Knowledge Transfer via Representation Distillation for Healthcare Collaboration Networks—0
Semi-decentralized Federated Ego Graph Learning for Recommendation—0
Privacy Against Agnostic Inference Attacks in Vertical Federated Learning—0
On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence—0
Designing Fairness in Autonomous Peer-to-peer Energy Trading—0
Offsite-Tuning: Transfer Learning without Full ModelCode2
Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes—0
Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety—0
Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract)—0
Towards a User Privacy-Aware Mobile Gaming App Installation Prediction Model—0
A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime DetectionCode1
z-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning—0
Federated Survival ForestsCode0
Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction—0
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference—0
GTV: Generating Tabular Data via Vertical Federated Learning—0
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imagingCode0
Federated Analytics: A survey—0
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss ApproximationsCode0
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds—0
Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data DistillationCode0
Differentially Private Distributed Bayesian Linear Regression with MCMCCode0
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation—0
Efficient Node Selection in Private Personalized Decentralized LearningCode0
Extracting Training Data from Diffusion ModelsCode1
Decentralized Entropic Optimal Transport for Distributed Distribution Comparison—0
Context-Aware Differential Privacy for Language Modeling—0
FedPH: Privacy-enhanced Heterogeneous Federated LearningCode0
SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-Device Inference—0
Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning—0
Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning—0
Accelerating Fair Federated Learning: Adaptive Federated Adam—0
Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer—0
Split Ways: Privacy-Preserving Training of Encrypted Data Using Split LearningCode0
Differentially Private Online Bayesian Estimation With Adaptive TruncationCode0
Federated Automatic Differentiation—0
Multi-Biometric Fuzzy Vault based on Face and Fingerprints—0
Binary Mechanisms under Privacy-Preserving Noise—0
Dual Personalization on Federated RecommendationCode1
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application—0
Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks—0
Federated Learning and Blockchain-enabled Fog-IoT Platform for Wearables in Predictive Healthcare—0
User-Centered Security in Natural Language Processing—0
A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Models—0
Privacy-Preserving Record Linkage for Cardinality Counting—0
STPrivacy: Spatio-Temporal Privacy-Preserving Action Recognition—0
PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time SeriesCode0
Recent Advances on Federated Learning: A Systematic Survey—0
Training Differentially Private Graph Neural Networks with Random Walk Sampling—0
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