Latest Articles for Data Privacy

Exploring LF QSDC as a secure solution for Web 3.0.

This article examines code licensing issues in AI model training.

A new method protects personal preferences while allowing useful rankings.

Improving eye-tracking systems through innovative training methods and data blending.

A new framework for training models without sharing sensitive data.

Learn how ghost sentences can help users safeguard their personal data online.

A method for generating synthetic data using Bayesian networks to protect privacy while allowing analysis.

A fresh approach to enhance privacy in IoT through hardware design.

Innovative protocols enhance machine learning while protecting sensitive data.

Explore STochastic Gradient Langevin Unlearning for data privacy in machine learning.

A new method enhances privacy in online federated learning while adapting to changing data.

This article explains how Deep Support Vectors improve understanding of AI decision-making.

This article reviews how well large language models protect sensitive information.

A new approach to optimize video streaming while ensuring user privacy.

Data Mesh transforms data management by fostering team ownership and collaboration.

A new model aims to simplify privacy policies for users.

Synthetic data offers promise for improving research on adolescent cancer patients.

This article examines the ethical concerns of using LLMs in research.

Examining content and motion replication in video generation models.

New methods improve model adaptability for diverse user tasks.

WhatsApp Explorer helps researchers ethically collect data while ensuring user privacy.

Learn how heavy-tailed noise enhances privacy in machine learning models.

Examining Weightless Neural Networks and their role in securing sensitive data.

Discover how DSL enhances IoT device collaboration and learning efficiency.

New method improves agent collaboration in diverse learning environments while maintaining data privacy.

A new approach to enhance recommendations while keeping user data private.

Meerkat-7B sets a new standard for open-source medical language models.

FedD2S enhances Federated Learning by personalizing models while preserving data privacy.

Explores the privacy risks and defenses related to synthetic data created by GANs.

A framework for training machine learning models while protecting privacy.

Exploring the role of ethics in language translation technology.

Exploring the impact of public data on differential privacy in machine learning.

Italy's ChatGPT ban affected software developers' productivity in unexpected ways.

A novel approach allows agents to optimize collaboratively with minimal data sharing.

A novel method improves decision tree aggregation while maintaining interpretability and privacy.

Discusses the risks of privacy in self-supervised learning and introduces PartCrop.

This study analyzes the effectiveness of synthetic images in face recognition systems.

Explore how to protect data privacy amidst growing AI technology.

This study examines methods for maintaining privacy in language processing tasks.

Synthetic data enables companies to share insights while protecting sensitive information.