K-Anonymity Sweeny came up with a formal protection model named k-anonymity What is K-Anonymity? If the information for each person contained in the release cannot be distinguished from at least k-1 individuals whose information also appears in the release. …

The (k, ϵ)-anonymity approach proposed in this paper first performs k-anonymisation on a subset of the quasi identifiers and then ϵ-differential privacy on the remaining quasi identifiers with different settings for each equivalence class of the k-anonymous dataset. k-anonymity, the parent of all privacy definitions - Ted \(k\)-anonymity is the oldest privacy definition, it's relatively simple to understand, so it has been quickly adopted by the healthcare community for their data anonymization needs. As a result, there has been a lot of research on how to transform a dataset into a \(k\) -anonymous table. database without compromising K-Anonymity individual privacy? individual privacy? • Models: K-Anonymity (Sweeney), Output Perturbation • K-Anonymity: attributes are suppressed or generalized until each row is identical with at least k-1 other rows. At this point the database is said to be k-anonymous. • K-Anonymity thus prevents definite database linkages. ℓ-Diversity: Privacy Beyondk-Anonymity privacy called k-anonymity[21, 24]. A table satisfies k-anonymity if every record in the table is indistinguishable from at least k − 1 other records with respect to every set of quasi-identifier attributes; such a table is called a k-anonymous table. Hence, for every combination of values of the quasi-identifiers in the k-anonymous table

-Diversity: Privacy Beyond k-Anonymity

To address the privacy issue, many approaches [1], [2] have been proposed in the literature over the past few years. Most of them are based on location perturbation and ob-fuscation, which employ well-known privacy metrics such as k-anonymity [3] and rely on a trusted third-party server. To achieve k-anonymity, a LBS related query is submitted

ℓ-Diversity: Privacy Beyondk-Anonymity

ACHIEVING k-ANONYMITY PRIVACY PROTECTION USING So, k-anonymity provides privacy protection by guaranteeing that each released record will relate to at least k individuals even if the records are directly linked to external information. This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity. K-Anonymity as a privacy measure - Propiedad Intelectual Jul 11, 2019