Latest Articles for Data Privacy

Exploring how SFL can transform data processing in future mobile networks.

Addressing privacy concerns in systems that manage sensitive data.

A new system improves identification accuracy and data privacy for wearable tech.

Asyn2F improves asynchronous federated learning for better model training and data privacy.

This framework allows collaborative model training while ensuring data privacy and model integrity.

Exploring the intersection of blockchain and federated learning for data privacy and security.

A method to protect privacy while sharing data insights across groups.

A new method to fine-tune models while ensuring data privacy.

Combining quantum computing and federated learning for enhanced data privacy.

This article discusses the fusion of foundation models and federated learning in healthcare.

This study explores Federated Learning's role in Document Visual Question Answering.

A novel approach improves merging of AI models for better performance and efficiency.

A method for collaborative machine learning while keeping data private.

Forgetting enhances learning in humans and machine models, improving adaptability and performance.

Quantum machine learning offers new ways to protect sensitive data.

UGradSL offers a practical solution for removing sensitive information from machine learning models.

Machine unlearning offers a way to improve data privacy in machine learning models.

This article discusses feature unlearning and its impact on privacy and fairness in machine learning.

A new system facilitates private use of LLMs through HPC resources.

Examining risks and defenses against backdoor attacks in AI models.

This article discusses machine unlearning and its implications for data privacy.

Robust Gray codes enhance data integrity even in noisy environments.

A method to improve machine learning while ensuring data privacy.

A study in Norway aiming to gather data on aging and dementia risk factors.

A look at how open-source models measure up against commercial counterparts in biomedical tasks.

Exploring how organizations adapt to large language models.

Examining privacy risks in model explanations and strategies to enhance security.

Exploring machine unlearning and its role in enhancing generative AI safety and privacy.

FedTime combines federated learning and local data for improved forecasting while ensuring data privacy.

A new framework improves federated learning efficiency while protecting data privacy.

Investigating how synthetic data aids healthcare advancements and addresses key challenges.

A system for secure AI learning in healthcare without sharing patient data.

Explore the significance of leverage scores in data analysis and privacy.

A look at crowd-sensing's benefits and challenges, focusing on privacy and user engagement.

A new approach to detect machine issues without compromising data privacy.

Researchers can study health conditions without accessing individual patient records.

An innovative approach to enhance resource management in IoT systems.

AI chatbots improve accuracy in diagnosing depression through advanced data analysis.

Exploring how LLMs are shaping the future of healthcare.

Examining the impact of AI technology on learning and its ethical implications.