  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.
