Anonymization Technique

Anonymization techniques aim to remove or obscure personally identifiable information (PII) from data while preserving its utility for analysis or other applications. Current research focuses on developing and evaluating methods for various data types, including images (using generative models like diffusion models and GANs), text (employing techniques like substitution, paraphrasing, and adversarial training), and even skeletal motion data (leveraging motion retargeting and adversarial learning). These advancements are crucial for responsible data sharing and analysis, particularly in sensitive domains like healthcare and law enforcement, balancing privacy protection with the need for data-driven insights.