Latest Articles for Data Privacy

A new quantum approach offers secure and efficient scalar product computation.

A look into privacy risks and defenses in Vertical Federated Learning.

Matcha helps developers create accurate privacy labels for mobile apps.

This article discusses the privacy concerns of using GPT models in cloud settings.

This research examines vulnerabilities in SNNs combined with federated learning techniques.

Exploring the importance of decentralized AI in data privacy and ethical concerns.

DSpodFL enhances decentralized federated learning by accommodating client differences.

New strategies in Federated Learning enhance privacy and efficiency in machine learning.

A new method improves synthetic electronic health records while preserving timing details.

This study examines privacy concerns in graph representation learning and edge reconstruction attacks.

Exploring risks of model inversion attacks on private data in machine learning models.

This study examines a method to enhance disease detection using multimodal data.

Examining generative models for synthetic gene expression data while ensuring patient privacy.

A deep dive into decentralized federated learning's key aspects and challenges.

Exploring how Differential Privacy protects personal data while providing valuable insights.

A new method improves data privacy and training efficiency in decentralized learning.

Flashback addresses forgetting in Federated Learning for improved model training and performance.

Innovative approaches for removing data from AI models while maintaining privacy.

New methods enhance privacy in machine learning by refining private selection mechanisms.

A new method enhances communication efficiency in federated learning.

Examining the relationship between data protection laws and machine learning practices.

TimEHR generates realistic synthetic time series data from electronic health records to aid research.

Learn how P3LS enables safe data sharing in manufacturing.

Learn how LLMs enhance recommender systems in various fields.

hFedF improves federated learning performance by tackling domain generalization challenges.

Exploring the balance between data collection and individual privacy rights.

A method for conducting Bayesian tests while safeguarding individual privacy.

Exploring methods to safeguard individual data in an information-driven world.

Learn how to protect user data in Android apps effectively.

A new method enhances LLMs by integrating external knowledge for better performance.

A new approach improves privacy for sharing multi-attribute data efficiently.

Study finds trade-offs between data privacy and bug reproduction.

Exploring fairness issues in machine learning and federated systems.

A look at how random walk algorithms improve privacy in decentralized learning.

Addressing ethical concerns through selective memory removal in AI models.

A new method enhances federated learning by addressing client differences.

Quantum computing enhances secure multiparty computation by improving privacy and efficiency.

This article covers methods to estimate bus speeds while protecting privacy.

A new method safeguards decision privacy in language models while maintaining performance.

Understanding unlearnable example attacks through game theory for better data protection.