Latest Articles for Data Privacy

ClipFL enhances federated learning by removing noisy devices for better performance.

A new method enhances synthetic data generation for clinical trials while ensuring privacy.

Combining federated learning and GNNs for improved stroke assessment while ensuring patient privacy.

A new method blends efficiency and accuracy in Federated Learning.

A look at protecting privacy in modern mobile networks with Open RAN.

Exploring how synthetic data protects privacy while enabling complex data analysis.

This article reviews strategies for improving deep learning in diverse medical image settings.

Addressing privacy and fairness in machine learning through innovative methods.

A new framework ensures fair performance across all devices in federated learning.

This article discusses protecting our personal data from language models.

Synthetic faces improve privacy while enhancing face recognition technology.

Trap-MID offers a clever way to protect data from hackers.

Discover how Federated Learning addresses data privacy in connected devices.

FedRewind improves collaboration among nodes in federated learning while maintaining data privacy.

This method finds top items while protecting personal data.

Discover methods for secure data analysis without compromising personal information.

Learn how devices train themselves while keeping your data safe.

A plugin safeguards federated learning models from harmful updates without compromising patient privacy.

New method improves skin lesion classification while protecting patient data.

This article explores machine unlearning and the benefits of the PruneLoRA method.

A new method enhances data analysis while preserving privacy.

This paper examines how timing differences in file systems can expose sensitive information.

Teaching machines to learn without revealing expert secrets is crucial for privacy.

FCLG helps analyze data from graphs without sharing sensitive information.

A new method allows safe data analysis for healthcare studies.

Explore federated learning, a method for training models without sharing personal data.

FedCoLLM connects large and small language models while ensuring privacy and efficiency.

Methods to safeguard sensitive data while maintaining model performance.

Assessing vulnerabilities in federated learning's privacy through attribute inference attacks.

Learn how machine unlearning aids data privacy and model accuracy.

Understanding the challenges and solutions for protecting privacy in data sharing.

Learn how federated learning tackles privacy while baking the perfect cookie recipe.

Explore the role of stochastic saddle point problems in recipe optimization and privacy.

Exploring the intersection of Auto-ML and Federated Learning for better data privacy.

Learn about a new method for safe and efficient file transfers.

Learn how string distances can aid privacy in sensitive data analysis.

Fake EHRs help in research and protect patient data.

Exploring how machines forget data while maintaining privacy and function.

A new approach to predicting energy needs while keeping data private.

FedRAV allows autonomous vehicles to learn collaboratively while keeping data private.