Which process generalizes data to protect individuals in a dataset by grouping or suppressing details?

• A. Aggregation and Banding
• B. Data Loss Prevention (DLP)
• C. Unencrypted Data (Cleartext/Plaintext)
• D. Alert

Answer: (A)

Generalizing data through aggregation and banding reduces the level of detail so individuals can’t be singled out. By grouping values into broader categories (like age bands or salary ranges) and combining data rather than showing exact values, the dataset preserves overall patterns while protecting privacy. For example, listing ages as 20–29 or 30–39 instead of exact ages, or showing salary ranges instead of precise salaries, makes it much harder to identify a person in the data. This approach is a common privacy technique because it directly lowers identifiability while still allowing useful analysis of trends and distributions.

The other concepts don’t fit because they address different aims: data loss prevention focuses on stopping sensitive data from leaving the system, unencrypted data refers to data exposed in plaintext, and an alert is merely a notification about something that happened, not a method for protecting individuals in a dataset. Aggregation and banding best describe the process of generalizing data to protect individuals.