Classification Task-Oriented Method of Differentially Private Data Publishing With Fine-Grained Correlations Preservation and Class Labels Preservation

Summary

This paper proposes a classification task-oriented method for differentially private data publishing. The method keeps class labels of data records unchanged while achieving differential privacy, uses a large language model to evaluate correlations among category attributes and merges highly correlated ones, and applies clustering to reduce the sensitivity of numerical attributes. Across the Adult, Bank, Statlog and Heart Disease datasets, the method's average F1 score improvements over other methods are 13.34%, 20.30%, 22.94% and 16.55%, respectively.