Latest Articles for Data Privacy

A method for agents to optimize solutions without central coordination.

A new method keeps images clear for humans while blocking unauthorized models.

This study examines the benefits of personalized responses in language models.

Seagull improves routing verification while ensuring privacy for network configurations.

Combining Federated Learning with privacy techniques protects sensitive data while training models.

New methods in federated learning protect against attacks while maintaining data privacy.

PPLR enhances privacy while improving recommendation system efficiency.

This article examines privacy threats in decentralized learning methods and the tactics of potential attackers.

Exploring the balance between privacy and learning efficiency in machine learning.

Researchers find ways to safeguard sensitive data in cooperative agent environments.

Watermarks can help protect copyright in AI model training by proving text usage.

Research shows long-term memory enhances health information sharing with chatbots.

A system to check fairness in machine learning while protecting model privacy.

A new framework enhances model performance while maintaining data privacy.

HFRec provides secure personalized course suggestions based on diverse student needs.

Examining how fine-tuning increases the risk of revealing sensitive training data.

A look into reconstruction attacks and their impact on data privacy in machine learning.

A method for collaborative analysis without sharing sensitive patient data.

A new system enables faster CNN training on devices with limited memory.

Examining the challenges and solutions in Collaborative Machine Learning for better privacy and safety.

A new method to improve machine learning models affected by poor data.

A novel cache attack exploits replacement policies to leak sensitive information.

This article examines machine unlearning in large language models.

CoDream enables organizations to collaborate securely without sharing sensitive data.

Discover how ESFL improves machine learning efficiency while protecting privacy.

New methods to safeguard sensitive data against unauthorized access in machine learning.

Addressing privacy concerns in machine learning with effective techniques.

Explore how synthetic datasets enhance machine learning performance and model selection.

Addressing the challenge of privacy in data-driven decision-making for healthcare.

Exploring how larger batch sizes improve differential privacy in machine learning.

FedReview improves federated learning by rejecting harmful model updates.

Examining the challenges of differential privacy in online learning systems.

Exploring the privacy and security risks linked to Large Language Models.

FedUV improves model performance in federated learning on non-IID data.

Exploring local differential privacy methods for secure graph analysis.

AerisAI enhances AI collaboration while protecting data privacy through decentralized methods.

Exploring differential privacy methods in reinforcement learning to protect sensitive data.

New methods secure data in AI while ensuring effective computations.

This article presents a method for clients with diverse objectives in federated bandit learning.

Discussing privacy and fairness in machine learning through differential privacy and worst-group risk.