Consistent Anonymization Effect

Consistent anonymization aims to remove personally identifiable information (PII) from various data types—including text, images, audio, and video—while preserving data utility for downstream tasks. Current research focuses on developing and evaluating anonymization techniques using diverse approaches, such as generative adversarial networks (GANs), large language models (LLMs), and differential privacy, often incorporating adversarial learning to enhance robustness against re-identification attacks. This field is crucial for responsible data sharing and AI development, enabling researchers and practitioners to leverage sensitive data while mitigating privacy risks and promoting ethical data usage.