Assembling The Fragments Of Privacy

The Exclusive Side Of Privacy

K-Anonymity

K-Anonymity is a privacy model used to ensure that any given record in a dataset is indistinguishable from at least k−1 other records with respect to certain identifying attributes. This means that for any set of identifying attributes (called quasi-identifiers), there are at least k records that have the same values for these attributes.

Example: Imagine a dataset of patient records with the attributes: ZIP code, Age, and Disease.

To achieve 2-anonymity, we need to make sure that for any combination of ZIP Code and Age, there are at least 2 records. Here’s the transformed dataset:

Now, each combination of ZIP Code and Age appears in at least 2 records, ensuring 2-anonymity.

L-Diversity

L-Diversity is an extension of k-anonymity that addresses its limitations by ensuring that sensitive attributes are also diverse within each group of quasi-identifiers. Specifically, it requires that there are at least lll “well-represented” values for the…