Latest Articles for Data Privacy

Combining federated learning and blockchain for secure model sharing.

FlexMod improves federated learning by optimizing resource allocation for diverse data types.

Introducing a new method for improving federated learning efficiency through tailored submodels.

A new framework improves privacy and efficiency in federated learning.

A method for automatic software bug fixing focusing on privacy and security.

Examining the need for transparency in the use of health data for research.

New methods enhance face recognition while addressing privacy concerns.

Analyzing the balance between decentralization and efficiency in outsourcing computation tasks.

ELM demonstrates the efficacy of smaller, context-specific language models in education.

New methods enhance data privacy while preserving analysis accuracy.

Exploring privacy risks in masked image modeling and their implications.

Pulse framework offers a new approach to detect unknown ransomware effectively.

Learn how Thompson Sampling protects privacy while making informed choices.

A method for collaborative edge inference prioritizing privacy and efficiency.

Research introduces a new dataset and methods for improved video ReID across platforms.

A guide to better comprehend differential privacy and its implications for personal data.

A new IDS enhances vehicle security against growing cyber threats.

Examining defensive methods to secure Federated Learning from data breaches.

A new method enhances communication in Federated Learning through optimal update scheduling.

A new system aims to enhance privacy in wireless communication and sensing.

A new framework improves model accuracy by tackling data imbalance while ensuring privacy.

A new approach to improve code summaries by including essential context.

Learn how federated learning enhances small object detection while maintaining data privacy.

MIA-Tuner aims to address privacy issues in LLM training data.

A new framework enhances privacy and data quality in federated learning.

Researchers propose a method to enhance medical models while ensuring data privacy.

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

A look at the challenges and benefits of adding new clients in federated learning.

This article discusses using pre-training to enhance privacy and performance in Federated Learning.

Combining model pruning and bandwidth allocation enhances efficiency in federated learning.

A new method improves tracking of privacy leaks in large language models.

This study analyzes FEWS and OEWS in enhancing model performance in manufacturing.

Exploring the role of privacy technologies in fighting financial crimes.

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

Examining vulnerabilities in code completion tools and their impact on user privacy.

Exploring security measures in hierarchical federated learning against various attacks.

Federated Clustering helps analyze data while keeping sensitive information private.

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

Introducing DUCD, a privacy-focused method to protect DNNs from adversarial attacks.

Examining copyright issues with machine learning in content generation.