Latest Articles for Data Privacy

This article presents a method for clients with diverse objectives in federated bandit learning.

Discussing privacy and fairness in machine learning through differential privacy and worst-group risk.

New algorithms enhance privacy and accuracy in sparse data scenarios.

A new method combines federated learning and secure computation to protect gaze data privacy.

BasedAI uses encryption to ensure privacy while enhancing language model performance.

A look at how data analysis can maintain individual privacy.

A method to remove unwanted skills from language models while keeping essential functions intact.

A novel method enhances energy load predictions while ensuring data privacy.

Asyn2F improves asynchronous federated learning for better model training and data privacy.

A new approach improves machine learning accuracy while ensuring data privacy.

A new approach to image representation with differential privacy through captioning.

A new method enhances federated learning efficiency using client update strategies.

Examining federated unlearning and its challenges in machine learning privacy.

Research shows how LLMs can expose training data, raising privacy concerns.

This article discusses privacy solutions for Max Cover and Set Cover problems.

A look into the risks of data poisoning in federated learning systems.

A new framework merges large and small models to prioritize user data protection.

Addressing challenges in federated learning due to diverse devices and data.

P2M2-CDR enhances recommendations while protecting user privacy through advanced data techniques.

Exploring privacy-preserving techniques in machine learning and their significance.

A novel approach enhances data recovery while addressing privacy concerns in federated learning.

New mechanisms enhance privacy while preserving data utility in machine learning.

Innovative algorithms use public data to protect privacy in data analysis.

A look at federated learning and its impact on business decision-making.

A method for agents to estimate functions without sharing data directly.

Investigating noise effects on training deep neural networks and privacy.

A novel decentralized approach improves learning performance in resource-limited IoT networks.

This research reveals privacy threats in simpler topic models like LDA.

A new framework enhances privacy in AI-generated images while ensuring efficiency.

This article discusses privacy methods for tabular data in large language models.

Learn how to safely share threat information among organizations.

Examining factors that influence trust in AI technology across diverse opinions.

A new framework to improve learning in Federated Incremental Learning while ensuring data privacy.

Exploring privacy risks related to membership inference attacks in machine learning.

New techniques to enhance privacy while maintaining recommendation quality.

New JAX-based library simplifies federated learning for better model training.

This study enhances distributed learning through effective use of weighted updates in Error Feedback.

A study on the fairness of privacy policies and their impact on user trust.

Exploring recent insights on synthetic data and privacy challenges.

A new scheme improves security and privacy in small cell networks.