Latest Articles for Privacy

A new algorithm enhances data exchange among vehicles while ensuring privacy.

A novel approach combines voice analysis with privacy protection for dementia detection.

A new system enhances data processing while ensuring user privacy and efficient resource use.

This article explores the benefits of using FP8 in federated learning.

Exploring methods to protect privacy while sharing network data effectively.

This study examines privacy issues and protection methods for AI classifiers.

A toolkit enhances privacy in text generation with on-device retrieval.

A new framework improves federated learning while ensuring data privacy.

Study reveals privacy concerns affecting census participation in subsidized housing.

A new approach enhances Federated Learning by generating synthetic data while protecting privacy.

Exploring the importance of data security in immersive art installations.

Exploring the importance of security and user feedback in e-payment apps.

Analyzing vulnerabilities in LLMs due to human preference data.

A new framework for agent communication focusing on privacy and consistency.

Exploring challenges and advancements in protecting sensitive data while maintaining its usefulness.

A new framework evaluates methods for anonymizing biometric data to enhance privacy.

A new method for efficient data deduplication in federated learning.

Exploring user-level differential privacy in large language model training.

Exploring the benefits of Federated Learning for anomaly detection in IoT networks.

A look at federated learning’s privacy and integrity challenges and solutions.

Regulations guide the safe and fair use of AI technologies across various sectors.

This paper presents methods to enhance model performance while ensuring data privacy.

A look at synthetic data generation for urban mobility and privacy challenges.

New methods are ensuring privacy in genomic data research.

New methods enhance privacy protection in large language models.

Examining privacy challenges faced by workers in home office environments.

Discover how synthetic data helps retailers protect customer privacy while gaining insights.

Exploring privacy risks in synthetic data and introducing the Data Plagiarism Index.

Exposing how third-party tools can compromise user privacy.

Study reveals surprising risks of sharing sensitive data with chatbots.

A new framework addresses fairness, privacy, interpretability, and emissions in AI.

This study examines trust and usability in electronic voting systems.

Exploring differential privacy to protect sensitive information in AI applications.

A robust dataset for training advanced chat-based AI systems.

PUFFLE offers a solution for privacy, utility, and fairness challenges in machine learning.

This study reveals new methods for reconstructing training data from high-resolution images.

Examining the advantages and dangers of AI in our daily mobile technology.

This method enhances privacy while improving model accuracy in AI.

Introducing a framework that balances privacy and effective social event detection.

A new method improves secure data analysis while protecting privacy.