Question

A major downside to k-anonymity is that re-identification is possible with

A: Multiple datasets
B: Sensitive columns
C: Database leaks
D: Expanding k values

Answer 1

A major downside to k-anonymity is that re-identification is possible with Multiple datasets, which threatens privacy even if the dataset is k-anonymized.

Step-by-step explanation:

A major downside to k-anonymity is that re-identification is possible with Multiple datasets. K-anonymity is a method used in privacy protection to ensure that individual records in a dataset cannot be distinguished from at least k-1 other records in the same dataset. However, if an attacker has access to additional external datasets, they may be able to cross-reference information and re-identify individuals, even if the original dataset adheres to k-anonymity principles. Sensitive columns, database leaks, or expanding k values can also contribute to privacy risks, but the combination of multiple datasets is a particularly significant threat to the integrity of k-anonymity.