Privacy Enhancing Technology

Privacy-enhancing technologies (PETs) aim to enable data utilization while safeguarding individual privacy, addressing growing concerns in various fields like machine learning and AI. Current research focuses on developing and improving PETs such as federated learning, differential privacy, and techniques for synthetic data generation, often applied within specific model architectures like Generative Adversarial Networks (GANs) and Graph Neural Networks (GNNs). These efforts are crucial for responsible AI development, balancing the benefits of data-driven advancements with the fundamental right to privacy, and impacting diverse applications from healthcare to autonomous vehicles. A key challenge lies in optimizing the trade-off between privacy protection and the accuracy or utility of the resulting data or models.