Latest Articles for Data Privacy

This method enhances privacy while improving model accuracy in AI.

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

A novel approach to enhance performance in hierarchical federated learning.

A new programming language improves MPC protocol security verification.

Federated learning offers a way to utilize data while maintaining user privacy.

This paper discusses the Poisson mechanism for maintaining privacy in data synthesis.

A new method improves secure data analysis while protecting privacy.

Exploring techniques for effectively removing data from AI models.

Exploring the role of spiking neural networks in vertical federated learning for data privacy.

A web-based tool making image analysis accessible for scientists without programming skills.

Exploring the use of watermarks to tackle copyright issues in language models.

This article explores security challenges and testing methods for 5G networks.

A new system enhances ML inference speed while preserving data privacy.

A new synthetic dataset enables accurate head detection and 3D modeling.

A look at the shuffle model's approach to protecting user data privacy.

New method enhances synthetic data generation while ensuring privacy protection.

A framework for ethical AI development in healthcare systems.

A detailed study on how models memorize text and its implications.

Exploring Quantum Local Differential Privacy to safeguard quantum data.

New techniques improve facial image generation for recognizing genetic conditions.

New methods enhance privacy and communication in federated learning.

Examining the privacy threats posed by ECG data in healthcare.

This study examines privacy differences between decentralized and centralized federated learning.

Examining differential privacy in natural language processing for better data protection.

This article examines how defacing affects MRI image quality assessments.

Examining privacy threats linked to social connections in digital spaces.

A framework enhances security in connected vehicles while protecting user privacy.

Innovative methods enhance website identification through network traffic analysis.

Defogger offers new strategies for exploring sensitive data while ensuring privacy.

This article discusses federated learning methods and their impact on privacy and model utility.

Examining collaborative CP-NIZKs for enhanced privacy and efficiency in proofs.

This study examines the effectiveness of clean-label physical backdoor attacks in deep neural networks.

Examining the FairJob dataset to ensure equal job opportunities for all.

A new method for secure key generation among devices in wireless networks.

New framework allows for efficient removal of sensitive data from Graph Neural Networks.

Exploring machine unlearning and its role in enhancing generative AI safety and privacy.

A new method ensures secure name matching while protecting sensitive data.

OpenUAS provides a detailed view of urban area usage across Japan's major cities.

LocPIR enhances privacy in location-based services through advanced encryption.

This framework enhances AI model access and efficiency using hybrid sharding.