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Privacy Preserving

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Showing 1851–1875 of 2975 papers

TitleStatusHype
KIPPS: Knowledge infusion in Privacy Preserving Synthetic Data Generation—0
Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data—0
Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification—0
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions—0
Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework—0
K-repeating Substrings: a String-Algorithmic Approach to Privacy-Preserving Publishing of Textual Data—0
Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution—0
Labels, Information, and Computation: Efficient Learning Using Sufficient Labels—0
LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation—0
LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimization—0
LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples—0
Language Modeling for the Future of Finance: A Quantitative Survey into Metrics, Tasks, and Data Opportunities—0
Language Models and Retrieval Augmented Generation for Automated Structured Data Extraction from Diagnostic Reports—0
Large language model as user daily behavior data generator: balancing population diversity and individual personality—0
Large Language Models Empowered Autonomous Edge AI for Connected Intelligence—0
Larger Model Causes Lower Classification Accuracy Under Differential Privacy: Reason and Solution—0
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks—0
Latent Dirichlet Allocation Model Training with Differential Privacy—0
Latent Noise Injection for Private and Statistically Aligned Synthetic Data Generation—0
LATTEO: A Framework to Support Learning Asynchronously Tempered with Trusted Execution and Obfuscation—0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
Learning data-derived privacy preserving representations from information metrics—0
Private Gradient Estimation is Useful for Generative Modeling—0
Learning Federated Representations and Recommendations with Limited Negatives—0
Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data—0
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