Latest Articles for Data Privacy

A look into how Sparse Models improve Federated Learning efficiency.

New AI model improves diagnosis of bone issues using X-ray and report data.

Learn how differential privacy can enhance data security in LLS regression.

A system that learns from patient data while ensuring privacy protections.

AI professionals share their views on Trustworthy AI and its challenges.

A new approach combines privacy protections with contextual bandit learning.

A new method for detecting vehicle misbehavior while protecting privacy.

A study on improving video stream detection while maintaining user privacy.

A method to erase identities from generative models while maintaining image quality.

New methods improve accuracy in join size estimation while protecting sensitive data.

A strategy to protect sensitive connections in graph data while releasing useful information.

A look at differential privacy and its application in machine learning.

A new method enhances decentralized federated learning while maintaining data privacy.

A new approach enhances intrusion detection in decentralized systems.

Improving mobile sensing applications through a novel framework for managing missing data.

This study examines how creators respond when their works are used for AI training.

This paper presents EFRAP, a defense against quantization-conditioned backdoor attacks in deep learning models.

A new method enhances the ability of models to forget specific visual information.

Methods to provide reliable population estimates while ensuring respondent confidentiality.

Research improves ECG interpretation while ensuring patient data privacy.

A new framework enhances federated learning and prevents forgetfulness in AI models.

A study reveals the WordGame attack, exploiting weaknesses in LLM safety measures.

Exploring how Federated Learning can enhance data privacy in healthcare.

A novel approach improves performance and fairness in federated learning.

New framework helps protect sensitive summary statistics during data sharing.

Introducing a new way to assess privacy risks in machine learning models.

Learn how distributed learning can improve machine learning efficiency and model training.

An overview of how anonymization safeguards personal identity while using biometrics.

Introducing a framework to balance data privacy and utility.

A new method to protect privacy in text-to-image models.

Examining the link between local differential privacy and fairness in ML systems.

New methods improve privacy auditing, focusing on hidden state models in machine learning.

Improving distributed learning through adaptive robust clipping techniques.

New method improves data removal in dynamic graph models while ensuring privacy.

Bayesian Federated Inference allows privacy-preserving survival analysis in cancer research.

FedSC improves model training while maintaining user privacy in federated learning.

New techniques improve scalability in differentially private machine learning.

Cohort-based approach improves efficiency and accuracy in Federated Learning systems.

Discover how Federated Behavioral Planes improve understanding of client actions in federated learning.

Introducing Alignment from Demonstrations for safe and effective language models.