Perturbation is the preferred technique for protecting medical data while still allowing for analysis.
Perturbation involves adding noise to the data to prevent the identification of individuals while preserving the overall patterns and trends in the dataset. This method is particularly useful in data mining for research purposes where privacy concerns must be addressed. The IAPP's CIPT materials cover this technique under data anonymization and de-identification strategies, emphasizing its importance in research and analytics.