Which technique aggregates data across individuals to reduce identifiability?

Which technique aggregates data across individuals to reduce identifiability?

• A. Generalization
• B. K-anonymity
• C. Aggregation
• D. Differential privacy

Answer: (C)

Aggregation protects privacy by pooling data from many individuals into a single summary, like a total, count, or average. This cross-person summary hides individual records and makes it difficult to pinpoint data about any single person. For example, reporting the average income of a neighborhood reveals a group characteristic without exposing individual incomes. Generalization focuses on making each data point less precise by using broader categories, while k-anonymity aims to ensure each record is indistinguishable from at least k−1 others, typically through generalization or suppression. Differential privacy adds random noise to outputs to limit what any result reveals about an individual, rather than creating a summary across the group. Because aggregation centers on combining information across people to produce a collective result, it best fits the described technique.

Aggregation protects privacy by pooling data from many individuals into a single summary, like a total, count, or average. This cross-person summary hides individual records and makes it difficult to pinpoint data about any single person. For example, reporting the average income of a neighborhood reveals a group characteristic without exposing individual incomes. Generalization focuses on making each data point less precise by using broader categories, while k-anonymity aims to ensure each record is indistinguishable from at least k−1 others, typically through generalization or suppression. Differential privacy adds random noise to outputs to limit what any result reveals about an individual, rather than creating a summary across the group. Because aggregation centers on combining information across people to produce a collective result, it best fits the described technique.