Generalization And Suppression
Generalization and suppression are data anonymization techniques used to protect privacy in datasets by reducing the granularity or removing sensitive information. Generalization involves replacing specific values with broader categories (e.g., replacing exact ages with age ranges), while suppression removes data entirely (e.g., omitting rare or identifying entries). These methods are fundamental in fields like data science, healthcare, and research to comply with privacy regulations while enabling analysis.
Developers should learn and apply generalization and suppression when handling sensitive data, such as in applications involving personal information, medical records, or financial data, to ensure compliance with privacy laws like GDPR or HIPAA. They are essential for creating anonymized datasets that allow for statistical analysis or machine learning without risking individual privacy breaches, particularly in data sharing, research, and public reporting scenarios.