Improving accuracy of classification models induced from anonymized datasets
We present a new anonymization algorithm for privacy-preserving data publishing. The non-homogeneous generalization is coupled with sensitive value distributions. The predictive utility of the algorithm is measured on eight anonymized datasets. The algorithm outperforms other methods with four classification techniques.
classification privacy preserving data publishing privacy preserving data mining k-anonymity ? ? -diversity ? ? ? ? ? non-homogeneous anonymization