Deidentification Algorithm

Deidentification algorithms aim to remove personally identifiable information from various data types, including text, images, and videos, to protect privacy while enabling data sharing and analysis. Current research focuses on developing robust and efficient algorithms, employing techniques like generative adversarial networks (GANs) for image and video anonymization, and leveraging unsupervised learning for text deidentification to overcome limitations of supervised methods. These advancements are crucial for addressing challenges in data privacy across diverse fields like healthcare and security, facilitating responsible data utilization while mitigating risks associated with sensitive information.