Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation

By Josep Domingo-Ferrer, Vicenç Torra. Data mining and knowledge discovery, 2005

492 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey of Inference Control Methods for Privacy-Preserving Data Mining (2008, methodology): “Microaggregation was suggested in [ 23 ] as a possible way to achieve k-anonymity for numerical, ordinal and nominal attributes.”
Privacy and Data Protection by Design - from policy to engineering (2014, background): “Later, [90] showed that microaggregation on the projection of records on their quasi-identifiers was also a valid approach.”
Towards trajectory anonymization: a generalization-based approach (2008, background): “Most clustering algorithms can easily be modified for k-anonymity by enforcing that the size of the clusters should be more than k [2, 38, 14, 1].”
Discrimination- and privacy-aware patterns (2015, background): “The computational procedures initially proposed for k-anonymity were generalization and suppression; microaggregation was proposed later as a natural alternative (Domingo-Ferrer and Torra 2005).”