Latest Articles for Data Privacy

A new method helps keep private data safe in language models.

Introducing FC-EM to protect 3D point cloud data from unauthorized access.

Examining the challenges and implications of unlearning in AI models.

A look at the FedQ-Advantage algorithm in federated reinforcement learning.

FedMap improves Federated Learning efficiency while ensuring data privacy.

New methods generate synthetic data to protect privacy in collaborative scenarios.

Local Differential Privacy enables secure data sharing while protecting individual user information.

A new approach to boxplots ensures data privacy while maintaining insightful analysis.

A novel approach combines voice analysis with privacy protection for dementia detection.

Collaborative training of medical algorithms while protecting patient privacy.

Examining the relationship between privacy techniques and biases in language models.

AAggFF introduces adaptive strategies for equitable model performance in Federated Learning.

New methods improve privacy and coherence using collocations in language data.

A new method for rewriting text that ensures privacy and maintains meaning.

Code poisoning enhances risks of membership inference attacks on sensitive data.

Research shows how easily safety features can be removed from Llama 3 models.

New techniques improve collaborative genomic studies while ensuring data privacy.

A new method reduces energy use while improving learning outcomes in decentralized systems.

QUEEN offers real-time protection against model extraction attacks in deep learning.

Fine-tuning large language models directly on smartphones while protecting user data.

A new method for efficient data retrieval in decentralized environments.

A framework for secure sharing of health information while protecting patient privacy.

This article explores the benefits of using FP8 in federated learning.

A look into how language models can leak sensitive data.

This article examines risks linked to LLMs and proposes ways to enhance safety.

Exploring legal challenges posed by generative AI's rapid evolution.

A new framework improves federated learning while ensuring data privacy.

This study assesses advanced models for generating fake trip data and their practical applications.

Venomancer is a stealthy backdoor attack on federated learning systems.

CLAMP-ViT offers a new way to compress vision transformers using synthetic data.

A detection system enhances vehicle security against cyber threats using advanced techniques.

New radar technology monitors heart rate during sleep and meditation without discomfort.

Zero-X enhances cybersecurity for connected vehicles against emerging threats.

A new framework improves efficiency in federated edge learning by optimizing resources and data.

Examining membership inference attacks on time-series forecasting models in healthcare.

Enhancing smart grid safety using honeypots and federated learning approaches.

An overview of methods to safeguard personal information while maintaining data usefulness.

A method for estimating community memberships while protecting individual privacy.

AntibotV tackles bot malware threats in connected cars through advanced monitoring.

Cities gain crucial transport data while addressing privacy concerns.