MC-GEN: Multi-level clustering for private synthetic data generation

With the development of machine learning and data science, data sharing is very common between companies and research institutes to avoid data scarcity. However, sharing original datasets that contain private information can cause privacy leakage. A reliable solution is to utilize private synthetic datasets which preserve statistical information from original datasets. In this paper, we propose MC-GEN, a privacy-preserving synthetic data generation method under differential privacy guarantee for machine learning classification tasks. MC-GEN applies multi-level clustering and differential private generative model to improve the utility of synthetic data. In the experimental evaluation, we evaluated the effects of parameters and the effectiveness of MC-GEN. The results showed that MC-GEN can achieve significant effectiveness under certain privacy guarantees on multiple classification tasks. Moreover, we compare MC-GEN with three existing methods. The results showed that MC-GEN outperforms other methods in terms of utility. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

With the development of machine learning and data science, data sharing is common to avoid data scarcity, but it can lead to privacy leakage. A reliable solution is to use private synthetic datasets that preserve statistical information. In this paper, we propose MC-GEN, a privacy-preserving synthetic data generation method for machine learning classification tasks. Experimental evaluation shows that MC-GEN achieves significant effectiveness under certain privacy guarantees and outperforms other methods in utility.