Which technique generalizes data to broader categories to reduce identifiability?

• A. Masking
• B. Pseudonymization
• C. Aggregation
• D. Generalization

Answer: (D)

Generalization is the practice of making data less precise by replacing exact values with broader categories. This reduces identifiability because exact attributes like a precise age or a precise ZIP code are swapped for wider groups such as an age range or a larger geographic area, making it harder to link the record to a specific person while still allowing useful analysis at a higher level. For example, turning age 33 into 30–39, or a detailed location into a city or region, preserves some information for analysis but blurs the fine details that could identify someone. Other techniques operate differently: masking hides parts of a value, pseudonymization substitutes identifiers with tokens, and aggregation reports summaries across groups rather than altering the granularity of individual attributes. Therefore, generalization best fits the idea of broadening data categories to reduce identifiability.