Latest Articles for Data Privacy

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.

Examining the challenges and implications of unlearning in AI models.

A look at the FedQ-Advantage algorithm in federated reinforcement learning.

FedMap improves Federated Learning efficiency while ensuring data privacy.

New methods generate synthetic data to protect privacy in collaborative scenarios.

Local Differential Privacy enables secure data sharing while protecting individual user information.

A new approach to boxplots ensures data privacy while maintaining insightful analysis.

A novel approach combines voice analysis with privacy protection for dementia detection.

Collaborative training of medical algorithms while protecting patient privacy.

Examining the relationship between privacy techniques and biases in language models.

AAggFF introduces adaptive strategies for equitable model performance in Federated Learning.

New methods improve privacy and coherence using collocations in language data.

A new method for rewriting text that ensures privacy and maintains meaning.

Code poisoning enhances risks of membership inference attacks on sensitive data.

Research shows how easily safety features can be removed from Llama 3 models.

New techniques improve collaborative genomic studies while ensuring data privacy.