Latest Articles for Data Privacy

A look at how federated learning and model pruning enhance wireless network performance.

LDP-Auditor framework estimates privacy loss in Local Differential Privacy methods.

Federated Learning improves speech recognition while keeping user data private.

A new approach balances privacy and performance in GNN training.

A new protocol aims to enhance privacy in card transactions.

This article explores a method to enhance federated learning with efficient communication.

This method improves brain disorder detection while keeping patient data secure.

EdgeFL simplifies federated learning while ensuring data privacy and efficiency.

A new method enhances privacy for images collected by fisheye cameras in self-driving cars.

Innovative methods enhance security in digital communication for sensitive information.

Steganalysis helps detect hidden messages in multimedia, ensuring secure communication.

New methods improve federated learning efficiency while ensuring data privacy.

This study assesses AI systems' effectiveness in guiding users on privacy policies.

How transfer learning improves threat detection and privacy in cybersecurity.

A new method using federated learning for large-scale mapping with neural radiance fields.

Research shows how LLMs can enhance privacy while maintaining language model effectiveness.

New methods improve model training while protecting user data privacy.

Exploring vulnerabilities and defense strategies in semantic communication systems.

Fairness as a Service tackles bias in machine learning systems securely.

Research seeks to improve infection monitoring and prevention in care homes.

New methods for preserving privacy while sharing information among multiple groups.

A method to address privacy and fairness concerns in machine learning.

A novel method that uses TEEs to protect machine learning models in federated learning.

Learning from Drift improves model performance in federated learning with diverse data.

FedDIP optimizes communication in federated learning through dynamic pruning and regularization.

This article examines the role of PETs in data privacy and their applications.

Examining the complexities of data privacy and unlearning in machine learning.

FedJudge offers a privacy-focused approach to legal language model training.

This work discusses synthetic data generation using differential privacy for economic studies.

A method combining Split Learning with Homomorphic Encryption enhances privacy in machine learning.

A method to optimize privacy settings for better data protection and utility.

A new framework ensures synthetic data answers are trustworthy for research.

A framework to ensure user privacy in GNNs while maintaining accuracy.

A look at challenges in Federated Learning from data reconstruction attacks.

A method enabling machine learning on encrypted data to protect user privacy.

A study on identifying the best option while ensuring data privacy.

This method enhances recommendations while protecting user data privacy.

Exploring ways to improve data sharing while ensuring user privacy.

Exploring differential privacy methods for secure data insights.

This article discusses a framework for managing privacy in multi-analyst scenarios.