Preservation of Anomalous Subgroups On Machine Learning Transformed Data

TL;DR

This paper explores how machine learning-based anonymization using variational autoencoders affects the preservation of anomalous subgroups in data, demonstrating that synthetic datasets can retain subgroup distinctions while ensuring privacy.

Contribution

It introduces a method combining anomaly detection and VAE-based anonymization to preserve subgroup information in synthetic data, forming the UGDP system.

Findings

Synthetic data preserved subgroup differentiation effectively.

The approach maintains data utility while enhancing privacy.

UGDP system is adaptable to different generative models.

Abstract

In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group's predicted odds ratio from the model and observed odds ratio from the data. We then perform anonymization using a variational autoencoder (VAE) to synthesize an entirely new dataset that would ideally be drawn from the distribution of the original data. We repeat the anomalous subgroup discovery task on the new data and compare it to what was identified pre-anonymization. We evaluated our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset. Finally, we packed the above end to end process into what we call Utility Guaranteed Deep Privacy (UGDP) system. UGDP can be easily extended to onboard alternative generative approaches such as GANs to synthesize tabular data.