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With the development of machine learning and data science, data sharing isvery common between companies and research institutes to avoid data scarcity.However, sharing original datasets that contain private information can causeprivacy leakage. A reliable solution is to utilize private synthetic datasetswhich preserve statistical information from original datasets. In this paper,we propose MC-GEN, a privacy-preserving synthetic data generation method underdifferential privacy guarantee for machine learning classification tasks.MC-GEN applies multi-level clustering and differential private generative modelto improve the utility of synthetic data. In the experimental evaluation, weevaluated the effects of parameters and the effectiveness of MC-GEN. Theresults showed that MC-GEN can achieve significant effectiveness under certainprivacy guarantees on multiple classification tasks. Moreover, we compareMC-GEN with three existing methods. The results showed that MC-GEN outperformsother methods in terms of utility.

MC-GEN:Multi-level Clustering for Private Synthetic Data Generation