Latest Articles for Data Privacy

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.

This method improves communication efficiency in federated learning while preserving data privacy.

Concerns grow over backdoor attacks in language models, impacting safety and reliability.

A method for calculating averages while keeping node data private.

Examining differential privacy's effectiveness in protecting individual identities in SOEP data.

A new model improves real-time detection of cyberattacks on blockchain networks.

FedSat addresses data challenges in federated learning, ensuring privacy and improved performance.

A new method for improved sampling in machine learning with enhanced privacy.

MSfusion allows resource-limited devices to train large models collaboratively.

New method improves model performance on unseen data.

AFIND+ improves decentralized federated learning by optimizing neighbor selection and update aggregation.

A new tool automates the anonymization of medical records for privacy protection.

Examine various jailbreak attacks on language models and their defenses.

A new approach enhances Federated Learning by generating synthetic data while protecting privacy.

This study examines how network layout affects decentralized federated learning amidst Byzantine threats.

pFedDIL improves machine learning by retaining knowledge while adapting to new tasks.

Research explores generative AI to simulate internal mouth structures using facial movements.

Analyzing vulnerabilities in LLMs due to human preference data.

A new framework for agent communication focusing on privacy and consistency.