Latest Articles for Data Privacy

Learn how personalization transforms video search experiences for users.

A look at Score Forgetting Distillation and its impact on generative AI.

Exploring the benefits and challenges of AI companions that remember user interactions.

Learn how differential privacy enhances data analysis while safeguarding personal information.

New methods enhance language models for individual user preferences.

New method MEOW tackles unlearning sensitive data in LLMs without losing performance.

A new method combines Federated Learning and Adversarial Training for insider threat detection.

A new AI-driven platform aims to enhance security in healthcare.

This article discusses privacy-focused data methods using federated learning and deep transfer hashing.

A new approach enhances federated learning processes for better data transmission.

Exploring how synthetic data safeguards privacy while enabling research access.

A new method helps language models forget specific information effectively.

This article examines a fresh approach to secure public-key encryption.

Combining Federated Learning and quantum technology promises safer data handling in AI.

Techniques to balance data distribution in federated learning for better model performance.

This article discusses methods to maintain privacy in optimization solutions.

A look at privacy concerns in centralized and decentralized learning systems.

Examining the privacy challenges associated with Federated Learning and gradient inversion attacks.

Innovative methods protect patient privacy while advancing medical research collaboration.

Examining the pitfalls of scaling AI models beyond necessity.

This approach improves efficiency in training models from large graph data while protecting privacy.

WAFeL improves device collaboration in federated learning while enhancing efficiency.

Balancing privacy and performance in AI through innovative unlearning techniques.

A look at data privacy issues in Android Automotive OS.

Exploring matrix factorization methods in data distributed across clients.

This framework simplifies understanding of privacy policies using AI technology.

CFVNet improves finger vein recognition accuracy and security through innovative techniques.

A method to enhance privacy in MIMO ISAC systems against potential threats.

A look into Hierarchical Federated Learning and its advantages for device privacy.

A new method enhances federated learning by reducing data transmission and energy usage.

A new approach uses synthetic data to improve pain recognition in videos.

This paper discusses methods for anonymizing networks while preserving data utility.

Exploring the importance of accountability in software systems affecting social and legal outcomes.

A new method enhances gradient estimation while maintaining data privacy in federated learning.

This benchmark evaluates privacy threats and defense mechanisms in NLP models.

This article focuses on identifying data misuse in text-to-image models.

A new method protects privacy while finding maximum values in distributed networks.

Selective encryption enhances privacy while maintaining model performance in collaborative learning.

A new method balances personalisation and privacy in federated learning.

Introducing a framework for secure training of machine learning models.