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.