Latest Articles for Data Privacy

Kernel Affine Hull Machines protect individual privacy while allowing data learning.

GLASU enhances communication efficiency in vertical federated learning using graph data.

A new method enhances device monitoring while safeguarding user privacy.

Study evaluates how well Android apps follow California's privacy law.

Introducing a new federated learning method for improved BEV perception in self-driving cars.

New methods generate medical images with limited data for better disease detection.

New method improves diabetic retinopathy staging and prediction reliability.

EZClone method reveals vulnerabilities in DNN architectures, posing risks to security.

GUIDED framework offers efficient unlearning for graph data, enhancing privacy protection.

Discussing poisoning attacks and defense strategies for Federated Recommender Systems.

A new technique ensures data privacy in collaborative machine learning.

New method enhances MRI interpretation using limited data from smaller clinics.

Effective placement of sensors in buildings protects privacy while collecting useful data.

A new framework improves smart grid security while maintaining data privacy.

An overview of differential privacy's role in protecting sensitive data.

Exploring secure methods for combining communication and sensing in mobile networks.

New methods create synthetic data to protect privacy in power systems research.

Utilizing typing patterns to improve online user verification.

New methods help create realistic healthcare data while protecting privacy.

New methods in machine unlearning improve data privacy and model efficiency.

Examining fairness in privacy methods for location data use.

Examining local differential privacy and numerical vector analysis for secure data sharing.

FeDiSa enhances security and efficiency in power systems against cyber threats.

APES and S-APES improve privacy in federated learning while maintaining accuracy.

GraphGANFed combines cutting-edge methods for molecule generation while ensuring data privacy.

New methods help hospitals diagnose COVID-19 while protecting patient privacy.

Survey reveals Australian cancer patients' views on data sharing practices.

A new approach enhances federated learning by reducing client drift and forgetting.

A look at the importance of protecting personal data in technology.

A new method empowers users to protect their data privacy in federated learning.

FedShift addresses class imbalance in federated learning for better model performance.

Explore federated learning's role in enhancing MIMO satellite communication efficiency.

This article evaluates re-identification risks in user representations and privacy measures.

SF-Relate enhances genomic studies by identifying relatives securely and efficiently.

A node-private algorithm for analyzing graph components protects individual privacy.

Exploring federated learning in decision-making with contextual bandits and privacy preservation.

Exploring ABS's approach to safeguard privacy while delivering essential statistical information.

Vers enhances distributed computing through decentralized data handling and robust security.

CIM systems improve efficiency but expose vulnerabilities to side-channel attacks.

MedAlpaca enhances medical AI models and training data for better patient care.