Question

K-anonymity in a dataset is achieved when each individual cannot be

A: Harmed from datasets with k individuals belonging to the sensitive class
B: Hidden from a quasi-identifier column as long as k individuals belong
C: Reidentified in k datasets
D: Distinguished from at least k individuals who are also in the dataset

Answer 1

K-anonymity ensures individuals in a dataset cannot be distinguished from at least k others, protecting privacy by preventing reidentification. It has applications in data privacy and can balance the need for privacy against the risks of discrimination due to anonymity.

Step-by-step explanation:

K-anonymity in a dataset is achieved when each individual cannot be distinguished from at least k individuals who are also in the dataset. This condition helps in protecting the privacy of individuals by ensuring that a person cannot be reidentified even if certain quasi-identifier information is available in the dataset. It is a concept commonly applied in the fields of data privacy and security to mitigate the risk of disclosing sensitive information. This approach is relevant in contexts described by Caroline Krafft where removing personal identifiers could reduce discrimination in hiring or service provision, yet complete anonymity could also lead to unintended consequences.