Latest Articles for Data Privacy

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.

Enhancing ASV systems to recognize children's voices accurately.

Examining how machine unlearning can expose sensitive data.

A novel approach to protect image privacy in technology while maintaining model performance.

Examining federated learning protocols to enhance privacy while improving model accuracy.

A look at the competition on synthetic datasets for face recognition technology.

A method to protect privacy while sharing data insights across groups.

This paper discusses how to personalize LLMs using retrieval methods for better user interactions.

A new approach to medical image analysis using federated learning and weak supervision.

A new tool for making images safer and more responsible.

Improving user experience through tailored responses in chatbots.

This project focuses on summarizing Privacy Policies using machine learning for better user comprehension.