Latest Articles for Data Privacy

A new mechanism enhances synthetic data generation while protecting individual privacy.

New methods enhance AI's ability to forget data while maintaining performance.

Examining the impact of data priors on differential privacy in image reconstruction attacks.

A new approach to simplify data access agreements.

New methods aim to enhance data removal in language models while preserving performance.

A new method enhances privacy in federated learning against gradient inversion attacks.

Exploring new privacy concerns in the use of diffusion models.

New methods enhance model learning by including slower devices in federated learning.

Research into smartwatch usage and data sharing comfort among users.

A novel approach to enhance summarization skills in smaller models using larger models.

A proposed framework enhances security for federated learning against adversarial attacks.

A new method for managing data flow while respecting user privacy choices.

This framework allows collaborative model training while ensuring data privacy and model integrity.

Learn how programming frameworks enhance differential privacy for data analysis.

LLMs are set to improve privacy and functionality on mobile devices.

Exploring methods to enhance privacy in decentralized learning while maintaining performance.

Machine unlearning allows models to forget data, essential for data privacy compliance.

New methods improve data security and efficiency for smart technology.

Researchers propose training models on altered images to minimize copyright issues.

Federated learning enhances cooperation between autonomous vehicles in challenging weather.

A study reveals new techniques for backdoor attacks on language models with minimal impact.

A look at the risks and assessments for Large Language Models in security.

A new framework for secure user data analysis in untrusted databases.

HETAL keeps client data secure while training machine learning models.

A new method for secure localization without compromising sensitive data.

FedMef improves federated learning for low-resource devices through innovative pruning techniques.

Examining the ethical concerns of Large Language Models in medicine.

New methods safeguard personal data in image generation.

PMixED improves privacy without sacrificing performance in large language models.

Exploring LF QSDC as a secure solution for Web 3.0.

This article examines code licensing issues in AI model training.

A new method protects personal preferences while allowing useful rankings.

Improving eye-tracking systems through innovative training methods and data blending.

A new framework for training models without sharing sensitive data.

Learn how ghost sentences can help users safeguard their personal data online.

A method for generating synthetic data using Bayesian networks to protect privacy while allowing analysis.

A fresh approach to enhance privacy in IoT through hardware design.

Innovative protocols enhance machine learning while protecting sensitive data.

Explore STochastic Gradient Langevin Unlearning for data privacy in machine learning.

A new method enhances privacy in online federated learning while adapting to changing data.