Latest Articles for Data Privacy

Web3 empowers users with control over data and online interactions through decentralization.

A new method uses GANs to create diverse medical images while ensuring patient privacy.

New algorithm improves vehicle safety and detects cyberattacks effectively.

A novel approach simplifies privacy attacks in machine learning models.

Innovative methods for clustering while ensuring differential privacy in changing datasets.

Examining challenges of RTBF in the context of Large Language Models.

Learn how to secure timely data exchange while preventing eavesdropping.

A look at differential privacy and its role in protecting sensitive data.

A new system improves mental health tracking through personal device data.

This paper presents a method for using GANs with privacy-protected data.

A new method to improve fairness in federated learning models while maintaining privacy.

A new library simplifies auditing methods for differential privacy to ensure data protection.

DBFed aims to reduce bias in AI while maintaining data privacy.

Examining pretrained transformers for multitask learning and communication efficiency in federated settings.

Fed-CPrompt enhances federated continual learning while preserving user privacy.

Examining methods to remove personal data from AI models effectively.

Examining vulnerabilities and solutions in the 5G communication landscape.

Examining how probabilistic hyperproperties can enhance program security.

A look at membership inference attacks and their implications for data privacy.

A look at the implications of data sharing between competing firms.

A new framework balances privacy and utility in graph learning.

Discover how federated learning enhances privacy while improving machine learning efficiency.

Study reveals public attitudes toward COVID-19 apps shifted over time.

A new approach to improve Federated Learning efficiency and model performance.

This research offers a way to create natural dialogues while respecting privacy.

Examining how users perceive privacy risks in sharing sensitive information.

Exploring how public data can improve privacy-preserving machine learning models.

A new method enhances speech recognition technology without losing previously learned knowledge.

A model to safeguard personal information from misuse in organizations.

Examining how differential privacy impacts graph neural networks in medical applications.

Learn how differentially private clustering protects individual data while analyzing trends.

Explore how differential privacy safeguards individual data during collective analysis.

This research introduces a method for detecting communities while ensuring data privacy.

A framework that improves federated learning for mobile devices, enhancing privacy and efficiency.

Examining the strengths and weaknesses of split learning in data privacy.

This paper explores methods to enhance accuracy in Local Differential Privacy mechanisms.

Innovative techniques aim to improve privacy in AI data analysis.

Exploring the impact of large language models on language teaching and assessment.

Examining how BadNets exploit spam filters through backdoor tactics.

New library enhances dataset creation for machine learning research.