Identification Risk

Identification risk, the probability of re-identifying individuals from anonymized data, is a critical concern across various fields, particularly healthcare and legal domains. Current research focuses on developing and evaluating methods to minimize this risk, employing techniques like masked language modeling, generative adversarial networks, and differential privacy to create synthetic data or transform existing data while preserving utility. These efforts aim to balance the need for data sharing for research and application development with the imperative to protect individual privacy, impacting the ethical and practical use of sensitive information.