Latest Articles for Data Privacy

Fed-Grow allows users to build larger models together while protecting privacy.

This article explores strategies for protecting individual privacy in machine learning.

Fed-RAA enhances federated learning by adapting to client resources for faster training.

A new approach in federated learning captures data dependencies while ensuring privacy.

A new benchmark for machine unlearning enhances evaluation and comparison of methods.

A new method reduces privacy risks in retrieval-augmented generation applications.

This article examines methods to remove user data from neural networks efficiently.

SeCTIS enables safe sharing of cyber threat intelligence while maintaining privacy and data quality.

A new method improves privacy protection in language models while maintaining performance.

Exploring privacy-preserving technologies in medical image analysis.

CEBench helps businesses and researchers assess LLMs while managing costs and performance.

Exploring techniques for privacy-preserving data analysis while ensuring accuracy.

New techniques enhance privacy and security in database queries and transactions.

A model that protects personal data in Italian legal writings.

This work enhances privacy in graph diffusion while maintaining data utility.

Exploring how user profiles improve personalization in language models.

TX-Phase enhances genotype imputation while ensuring data privacy for researchers.

Learn how differential privacy protects individual data while allowing useful analysis.

Combining IoT and blockchain enhances privacy and security for location tracking.

Crepe empowers researchers to collect mobile screen data easily.

TimeAutoDiff offers new solutions for creating realistic synthetic time series data.

This study examines the role of individual data in agent-based modeling.

Lomas enables secure access to public data for research without compromising privacy.

This method enhances transparency in determining document authorship.

Addressing biases in face recognition through balanced training datasets.

AUTOPRIV simplifies data privacy for users without technical skills.

New methods leverage language models to enhance activity recognition in smart homes.

A flexible approach for generating CFEs that respects data privacy concerns.

Learn about cryptography's role in securing information and protecting privacy.

A new method enhances memory and adaptability in medical imaging models.

FedBiOT improves large language models while keeping data private and resource usage low.

Techniques to enhance performance in training models with differential privacy.

QFHE allows computations on encrypted data for enhanced privacy and security.

This article presents a method to protect personal data in machine learning systems.

How federated learning faces privacy threats while striving for data security.

A new method enhances LLM personalization for better user engagement.

New methods reveal serious privacy threats from location data sharing.

New method promises efficient data erasure while preserving model performance.

A new method helps keep private data safe in language models.

Introducing FC-EM to protect 3D point cloud data from unauthorized access.