De Identification

De-identification aims to remove personally identifiable information (PII) from data, enabling responsible data sharing while protecting privacy. Current research heavily utilizes deep learning models, particularly transformer architectures like BERT and its variants, alongside rule-based and hybrid approaches, to identify and redact or replace PII in various data types including text, images, and audio. This is crucial for advancing research in sensitive fields like healthcare and law enforcement, where access to large datasets is essential but privacy concerns are paramount, and is driving the development of robust and accurate de-identification techniques. The field also focuses on mitigating biases in de-identification algorithms and evaluating the effectiveness of different methods against various attacks, ensuring true privacy preservation.