Privacy Preserving Image

Privacy-preserving image processing aims to enable the use of image data for machine learning and other applications while safeguarding sensitive information. Current research focuses on techniques like federated learning, differential privacy, and generative adversarial networks (GANs), often applied within architectures such as vision transformers and convolutional neural networks, to achieve this balance between utility and privacy. These methods address challenges in image classification, retrieval, and registration, with a strong emphasis on improving accuracy and fairness while maintaining robust privacy guarantees. The field's impact spans various sectors, including healthcare and security, where sensitive visual data is crucial but privacy is paramount.