Which method involves removing information that can uniquely identify an individual from data so that it can be shared without violating privacy laws and regulations?

Anonymization is the correct method that involves the removal of information capable of uniquely identifying an individual from data sets, allowing the remaining data to be shared without violating privacy laws and regulations. This process ensures that individuals cannot be re-identified, thereby protecting their privacy while still permitting the utilization of the underlying data for analysis or research.

Anonymization transforms data by using various techniques such as data masking or perturbation, effectively severing the link between identifiably personal information and the data being analyzed. This is particularly important in fields like healthcare or finance, where sensitive data must comply with stringent privacy standards. The key principle is that once data is anonymized, it cannot be traced back to an individual, fostering data sharing while maintaining privacy.

In contrast, other methods such as tokenization replace sensitive data with non-sensitive equivalents (tokens) but may retain a mapping back to the original data, which does not provide the same level of privacy protection as anonymization. Scrubbing generally refers to cleaning or cleansing data but does not specifically address the anonymization of identifiable information. Integrity management focuses on ensuring the accuracy and completeness of data rather than on its privacy or the ability to share it responsibly.