Which approach ensures that sensitive data is anonymized before being used in public datasets?

The approach that ensures sensitive data is anonymized before being used in public datasets is data anonymization. This technique involves altering personally identifiable information (PII) or any sensitive data, rendering it impossible to identify the original source or individuals to whom the data pertains. By employing data anonymization, organizations can share datasets for research or public use without risking the exposure of personal information, thus complying with regulations like GDPR or HIPAA that emphasize the protection of sensitive data.

Data anonymization encompasses various methodologies, such as aggregation, where individual data points are combined to create generalized information. This allows for analysis and insight without compromising individual privacy. In contrast, while data masking and encryption are valid techniques for protecting data, their primary focuses differ. Data masking alters data for various uses but doesn't prevent identification in all cases, and encryption secures data through transformation but does not inherently anonymize it. Data auditing, on the other hand, involves reviewing data access and usage patterns rather than altering the data itself for anonymity.