Maximum associate attribute based decomposition for privacy preserving publication
Feng Jianhua · Journal of Tsinghua University(Science and Technology) · 2011
Privacy preserving data publication results in significant information loss caused by generalization when dealing with high-dimensional data.Decomposition improves the result by preserving exact values,but the released data still has low utility because the correlations between sensitive attributes and the other attributes are destroyed.A maximum associated attribute based decomposition model was developed to help determine the views for release.By using the frequent items mining technique,this model can find the attribute sets with strong associations.An anonymization principle λ-matching is used for multiple views publication.Tests demonstrate that the multiple views produced by this model are good for data mining since they preserve more correlations between the attributes than ordinary decomposition.