In the k-anonymity model, the value k may be used as a measure of privacy: the higher the value of k, the harder it is to de-anonymize records. Currently, there are many algorithms to implement k-anonymity. Mondrian is a Top-down greedy data anonymization algorithm for relational dataset. A k-d tree, or k-dimensional tree, is a data structure used

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 A new technique ensuring privacy in big data: K-anonymity Although, many k-anonymity algorithms have been proposed, most of them consider that the privacy parameter k of k-anonymity has to be known before applying the k-anonymity process. For example, Yonghong Xie et al. in [5] made a combination of diverse techniques to ensure privacy of medical data. User k-anonymity for privacy preserving data mining of May 01, 2012 Mondrian Multidimensional K-Anonymity

the privacy of users is not dying out. K-anonymity is a way to achieve this. It requires each tuple in the published table be indistinguishable from at least . k-1 other tuples. There . are three kind of attribute in . k - anonymity. First is key attribute which is generally the name, ID etc. and it is

k-anonymity is a lexical transformation on the DB. It's just mapping the universe of all possible DBs to all those which respect the k-anonymity principle. Thus, exactly as you say, it is just a property of the DB. Poor values of k or cases when the distance between the original DB and the mapped DB is large can lead to significant privacy leaks.

k-ANONYMITY: A model for protecting privacy

k-anonymity - Data Privacy Lab The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, m-Argus and k-Similar provide guarantees of privacy protection. Keywords: data anonymity, data privacy, re-identification, data fusion, privacy Citation: L. Sweeney. k-anonymity: a model for protecting privacy. k-anonymity: a model for protecting privacy: International The k-anonymity protection model is important because it forms the basis on which the real-world systems known as Datafly, µ-Argus and k-Similar provide guarantees of privacy protection. References