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

TL;DR

MC-GEN is a novel multi-level clustering approach that generates private synthetic datasets with differential privacy guarantees, improving utility for machine learning classification tasks compared to existing methods.

Contribution

The paper introduces MC-GEN, a new differential privacy-based synthetic data generation method utilizing multi-level clustering to enhance data utility.

Findings

MC-GEN achieves high utility under privacy constraints.

It outperforms three existing synthetic data generation methods.

Effective across multiple classification tasks.

Abstract

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.