Latest Articles for Data Privacy

Research highlights model robustness and defenses in decentralized federated learning.

A new method improves AI performance using public datasets while protecting patient privacy.

New methods tackle privacy risks in human movement data prediction.

This paper presents methods to enhance model performance while ensuring data privacy.

A fresh method to compare privacy mechanisms in machine learning.

A new method improves privacy while training deep learning models.

This article discusses machine unlearning and its implications for data privacy.

A novel approach to enhance security in federated learning against backdoor attacks.

A new framework aids small developers in creating RoPA using user experiences.

New methods for federated learning improve efficiency and privacy in IoT networks.

A look at synthetic data generation for urban mobility and privacy challenges.

New methods are ensuring privacy in genomic data research.

Introducing FedGTG to retain knowledge while learning in federated settings.

New methods enhance privacy protection in large language models.

A new method enhances the security of deep learning models against hidden threats.

Synthetic data generation aids healthcare research while protecting patient privacy.

New models improve tissue image quality for better disease diagnosis.

MedUniverse enhances medical imaging tools while protecting patient privacy.

This article discusses retraining methods using model predictions for improved accuracy.

Discover how synthetic data helps retailers protect customer privacy while gaining insights.

Exploring privacy risks in synthetic data and introducing the Data Plagiarism Index.

Analyzing effective clean-label backdoor attack techniques in machine learning.

Federated learning enhances medical imaging while protecting patient data.

Memory encryption offers a new way to keep cloud data safe and efficient.

Training DNNs on microcontrollers boosts efficiency and privacy in smart technology.

A novel approach uses quantum states to compare private information securely.

NeighborFL enhances traffic prediction accuracy while protecting data privacy.

Examining the significance of privacy through identity unlearning in machine learning.

A new method improves the efficiency of machine unlearning while preserving model performance.

A closer look at new methods for text anonymization and their benefits.

A method to improve machine learning while ensuring data privacy.

Collaboration in healthcare through federated learning improves medical image classification while protecting privacy.

PSVAE offers a faster method for creating high-quality synthetic tabular data.

A framework for assessing Federated Learning models in real-world scenarios.

A new method improves the prediction of hospital stay lengths while protecting patient privacy.

A new approach enhances model training while protecting data privacy.

A study on network traffic characteristics of medical devices for better security.

Examining differential privacy and NTK regression to protect user data in AI.

A look at how open-source models measure up against commercial counterparts in biomedical tasks.

A closer look at disclosure risks in synthetic data and privacy protection.