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Privacy Preserving

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Showing 1951–1975 of 2975 papers

TitleStatusHype
Secure multiparty computations in floating-point arithmetic—0
SFPDML: Securer and Faster Privacy-Preserving Distributed Machine Learning based on MKTFHE—0
Secure Social Recommendation based on Secret Sharing—0
Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks—0
Secure Sum Outperforms Homomorphic Encryption in (Current) Collaborative Deep Learning—0
Secure Visual Data Processing via Federated Learning—0
Securing Federated Learning against Overwhelming Collusive Attackers—0
Securing Genomic Data Against Inference Attacks in Federated Learning Environments—0
Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach—0
Securing the Future: Innovations in Privacy-Preserving Integrated Sensing for Industrial Cyber-Physical Systems—0
Security and Privacy Preserving Deep Learning—0
Security of Internet of Agents: Attacks and Countermeasures—0
Security Protection in Cooperative Control of Multi-agent Systems—0
Contrasting Adversarial Perturbations: The Space of Harmless Perturbations—0
See through Gradients: Image Batch Recovery via GradInversion—0
SegLoc: Learning Segmentation-Based Representations for Privacy-Preserving Visual Localization—0
Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition—0
Selective Interpretable and Motion Consistent Privacy Attribute Obfuscation for Action Recognition—0
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos—0
Semi-Decentralized Federated Edge Learning with Data and Device Heterogeneity—0
Semi-decentralized Federated Ego Graph Learning for Recommendation—0
Sensing-Resistance-Oriented Beamforming for Privacy Protection from ISAC Devices—0
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption—0
SF-PATE: Scalable, Fair, and Private Aggregation of Teacher Ensembles—0
SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices—0
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