Latest Articles for Data Privacy

A look at methods improving privacy in federated learning while ensuring model accuracy.

A method that customizes LLMs while protecting users' sensitive data.

Examining factors influencing the use of mobile health applications across various demographics.

Examining k-anonymity and synthetic data for privacy and energy use in AI.

A unique dataset reveals mobile app usage patterns in France's urban areas.

Exploring new solutions for secure and efficient 6G communication technologies.

Examining how privacy measures affect school attendance boundaries and diversity.

New method generates diverse synthetic images for better facial recognition.

Learn how machine unlearning protects personal information in ML models.

Forgetting is key to managing knowledge in AI and decision-making.

This article explores the risks and ethical concerns surrounding ChatGPT.

Explore how CRSs use dialogue for better content recommendations.

Explore how traceable mixnets enhance privacy in data management while allowing specific queries.

FedAds offers a solution for effective CVR estimation while safeguarding user privacy.

This article examines the threats in federated recommender systems using visual information.

ResidualPlanner provides fast, private insights from data using noisy marginals.

A new method predicts taxi demand while ensuring data privacy.

SplitAMC enhances data privacy and reduces latency in modulation classification.

Explore the methods and challenges of model evaluation in federated learning.

New method audits privacy in machine learning with a single algorithm run.

FLARE helps IoT devices manage concept drift while ensuring privacy and efficiency.

Parametric insurance offers faster claims and increased transparency through technology.

Explore how vertical federated learning boosts privacy in machine learning.

A new method improves Federated Learning efficiency by adjusting local training steps.

Improving synthetic data methods can lead to more reliable machine learning models.

PrivaScissors minimizes data leakage in collaborative inference, enhancing user privacy.

A new method improves image segmentation while maintaining patient privacy.

A method enabling selective forgetting in generative models to prevent misuse.

Synthetic images help improve face recognition while addressing data collection issues.

Study reveals significant bias in de-identification methods for clinical names.

Federated learning offers solutions for fairness in AI-driven healthcare predictions.

A new method for training models while protecting sensitive data.

A robust approach to create unlearnable examples for data protection.

New methods show promise in safeguarding personal data against unauthorized use.

A new method enhances data sharing among connected vehicles using federated learning.

Enhancing efficiency in Federated Learning with risk-aware device strategies.

Exploring the importance of trust in federated learning systems.

PS-FedGAN enhances privacy and efficiency in federated learning by sharing minimal data.

Federated Learning enhances model training while keeping user data private.

Random relabeling offers a method for quick data removal in machine learning.