Latest Articles for Data Privacy

Exploring the role of privacy technologies in fighting financial crimes.

Explore the significance of leverage scores in data analysis and privacy.

Examining vulnerabilities in code completion tools and their impact on user privacy.

Exploring security measures in hierarchical federated learning against various attacks.

Federated Clustering helps analyze data while keeping sensitive information private.

A look at crowd-sensing's benefits and challenges, focusing on privacy and user engagement.

Introducing DUCD, a privacy-focused method to protect DNNs from adversarial attacks.

Examining copyright issues with machine learning in content generation.

A new approach to detect machine issues without compromising data privacy.

Exploring confounding privacy and its role in protecting sensitive information.

Examining the role of language models in transforming medical applications.

Researchers can study health conditions without accessing individual patient records.

Innovative algorithms for counting distinct items while ensuring data privacy.

A detailed study on the security of Olm and Megolm protocols in Matrix.

A guide to ethical requirements for developing responsible AI systems.

A new dataset simulates psychiatrist-patient dialogues for mental health research.

Exploring certified unlearning and its role in data privacy protection.

A new framework improves audio classification by leveraging multi-modal device knowledge.

New methods enhance privacy in sharing electricity usage data while maintaining its usefulness.

Learn how language models on devices improve speed and privacy.

This article explores zero-shot quantization and its applications in infrared imaging.

New techniques using gamma family distribution improve privacy in sensitive datasets.

An innovative approach to enhance resource management in IoT systems.

Celtibero provides strong protection against attacks in federated learning systems.

New methods enhance accuracy in machine learning with limited data labeling.

Improving data analysis accuracy while ensuring user privacy through ULDP.

Explore federated learning's approach to privacy in machine learning.

A new approach to improve fairness and accuracy in facial recognition.

This article discusses a new method to protect personal data while maintaining its usefulness.

A web platform for private data analysis in health and genomics.

Examining memorization in language models and sampling techniques.

OpenFGL offers a comprehensive framework to evaluate federated graph learning methods effectively.

This article discusses ethical issues in computer vision, focusing on privacy and bias.

A solution for tracing products to enhance security against counterfeiting.

Evaluating the hurdles organizations face when applying differential privacy in survey data.

Using AI to simulate data for analyzing emotional connections in mental health.

Discover how Federated Learning keeps data private while enabling collaboration in IIoT.

This article presents a new method for realistic dialogue systems using user-specific traits.

A new method for adapting deep learning models to unlabeled data while ensuring privacy.

A workshop discussing challenges and solutions for platform workers.