Latest Articles for Data Privacy

New algorithms improve privacy and optimization in machine learning models.

New methods enhance patient privacy while improving medical image research.

Analyzing threats and defenses in federated learning against malicious attacks.

Exploring privacy risks and solutions in large language models.

Exploring the vulnerabilities of ML models and potential defenses against MIAs.

EncDB offers improved security and efficiency for managing encrypted data in the cloud.

A look at improving IoT security using existing sensor data for authentication.

Researchers aim to generate balanced synthetic data to prevent bias in machine learning.

A new approach to enhance e-health through improved data management and patient privacy.

A new algorithm enhances federated learning by addressing client diversity and efficiency.

New regulations hinder medical research progress and collaboration in Finland.

An overview of legal challenges in generative AI development and use.

A method to protect data privacy in decentralized learning systems using virtual nodes.

A look at how algorithms can respect data deletion requests while maintaining efficiency.

A new tool aims to make legal agreements easier to read.

A study on improving communication in federated learning for better model performance.

A new approach to sharing trajectory data while maintaining user privacy.

This article examines how preprocessing steps can impact data privacy guarantees.

S3PHER empowers patients by enabling secure health data sharing with providers.

A look at using language models to evaluate software requirements satisfaction.

A new defense mechanism reduces label inference attack risks in collaborative machine learning.

This study enhances federated learning by boosting model diversity while protecting privacy.

A new approach for agent-based modeling safeguards individual privacy while maintaining data accuracy.

The DPV offers a clear framework for personal data handling.

A framework to enhance collaboration in healthcare while ensuring patient privacy.

Strategies for analyzing sensitive data while maintaining privacy.

Ensuring technology design respects user diversity and context.

Exploring how BR-DP balances privacy and data analysis.

Introducing UGEs, a new way to keep data safe and usable.

Exploring fine-tuning methods to improve model accuracy while ensuring data privacy.

Examining the risks of model poisoning attacks in federated learning systems.

This article explores methods to protect privacy while analyzing data effectively.

A secure protocol for protecting user data in machine learning.

Robots optimize movement using a new collaborative learning technique.

DA-DPFL improves federated learning by reducing costs and training time.

Exploring the role of synthetic iris images in biometric systems.

Improving federated learning with hierarchical structures and smart data handling.

Optimizing noise generation methods for better data privacy in streaming applications.

New methods enhance privacy and dropout resilience in decentralized learning.

A new method tackles Byzantine threats while protecting user data privacy.