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TableĀ 1 Description of PPDM methods

From: A comprehensive review on privacy preserving data mining

PPDM methods Description
Data distribution May contain vertically or horizontally partitioned data
Data distortion Contain perturbation, blocking, aggregation or merging, swapping and sampling
Data mining algorithms Encloses classification mining, association rule mining, clustering, and Bayesian networks etc
Data or rules hidden Denotes to hide main data or rules of innovative data
K-anonymity Achieve the anonymization
L-diverse Keeps the least group size of K, and maintains the diversity of the sensitive attributes
Taxonomy Tree Attributes the generalization to limit the information leakage
Randomization An un-sophisticated and valuable technique to hide the individual data in PPDM
Privacy protection Protects the privacy, it should adapt data carefully to attain optimum data utility