Which data protection method modifies existing data to remove sensitive elements?

The method that modifies existing data to remove sensitive elements is data scrubbing. This process involves cleaning and preparing the data by identifying and removing or masking sensitive and irrelevant information from datasets. The goal is to enhance data accuracy and utility while ensuring that sensitive information is handled appropriately, often for compliance with regulations or to protect privacy.

In contrast, tokenization substitutes sensitive data with unique identifiers or tokens that can be mapped back to the original data without exposing it. Anonymization involves altering data so that individuals cannot be identified, even in combination with other data, but does not necessarily involve modifying the original dataset directly. Reidentification attempts to restore the original sensitive information from anonymized data, which is not a data protection method in the same context as scrubbing.

Thus, data scrubbing is the best-fit choice for the described method of modifying data to remove sensitive elements.