Latest Articles for Data Privacy

A framework to develop personalized policies using observational data while ensuring privacy.

A method for secure and efficient use of LLMs while protecting user privacy.

KARD enhances small models by integrating external knowledge for better reasoning.

Examining how selective classifiers preserve privacy and prediction accuracy.

Examining the interplay of communication methods and privacy in virtual spaces.

A look at how differential privacy safeguards individual information in data analysis.

This paper discusses methods to enhance communication efficiency in split learning while preserving model performance.

A deep dive into face morphing attacks and detection methods.

A look at differentially private convex optimization for data protection in machine learning.

A study on the effectiveness of neighborhood attacks in revealing training data.

Examining the conflict between data access and confidentiality in research.

A new framework for federated learning adapts to shifting data while ensuring privacy.

This paper examines the importance of statistical indistinguishability in learning algorithms.

Examining the effectiveness and challenges of unlearnable datasets in protecting private information.

Federated learning improves model training while keeping user data private.

A new method ensures privacy in speech classification without sacrificing performance.

Using federated learning to enhance speech analysis for Parkinson's diagnosis across languages.

New models improve air-to-ground communication using federated learning and generative neural networks.

A method for training models that protects data privacy while improving collaboration.

A new framework improves federated learning speed and efficiency while protecting privacy.

Balancing user privacy and decision-making in AI with differential privacy techniques.

A new framework improves remote sensing data analysis while maintaining privacy.

This article examines privacy models' effects on monitoring CO2 levels in smart buildings.

This article examines attribute inference attacks on GNNs and their impact on privacy.

This article explores the benefits and challenges of decentralized federated learning.

Combining federated learning and MIMO enhances privacy and efficiency in wireless systems.

A look at ensuring reliability in auditable data structures.

FedMSA enhances federated learning by improving communication and efficiency in decentralized training.

Addressing privacy and accuracy in synthetic data analysis through Bayesian inference.

New methods enhance privacy while preserving text meaning and structure.

New methods improve face recognition accuracy while addressing privacy concerns.

A new framework enhances content delivery in busy urban areas.

Study assesses privacy risks of generative models in medical data sharing.

Framework aims to protect patient privacy in sharing synthetic medical data.

GPT-FL framework improves federated learning by using synthetic data.

A method to remove data from machine learning models while ensuring privacy.

A new method improves privacy and accuracy for high-dimensional data.

Exploring privacy-preserving learning while tackling communication issues in federated reinforcement learning.

A method to secure sensitive data in machine learning using information flow control.

This article discusses data privacy risks and detection methods in federated learning.