… In this paper, we presented three methods for k-anonymity in statistical databases using
microaggregation. We analyzed these methods and reported their main advantages and short…

… means that we would select the k-anonymity solution with the maximum generalization as the
… In other words, we will use a modified version of DM*, as calculated in online Appendix D. …

… k-anonymity model and presents enhanced k-anonymity models. This paper reviews problems
of the k-anonymity … introduced kanonymity model and discussed how it protects private …

… in privacy algorithms of big data. This paper first introduces two privacy models, namely, k-anonymity …
a greedy algorithm for preserving privacy using k-anonymity privacy model. The …

… The motivating factor behind the k-anonymity is that many attributes in the … k-anonymity
techniques such as bottom-up generalization, top-down specialization and k-anonymity using …

… The protection provided by k-anonymity is easy and simple to understand like if any table
has k-anonymity for some value m than the one who knows the quasi identifier of someone …

… k-Anonymity technique executes in O (n2/k) time. Author practically compares their techniques
with other clustering based k-Anonymity … , “Achieving k-anonymity privacy protection using …

… There are two kinds of major attacks against privacy namely record linkage and attribute …
k-anonymity, ℓdiversity, t-closeness for data privacy. K-anonymity method preserves the privacy …

… k-anonymity k-anonymity is a privacy preserving paradigm to ensure distinct records in a
specific dataset cannot be identified. The datasets are said to be k-anonymous only when a …

… k-Anonymity: The essential arrangement of kanonymity is to shield a dataset against re-identified
by summing up the characteristics that may be used in a linkage attacks (semi …