2019/11/09 by Samuel C. Maina, Reginald Bryant, Reginald E. Bryant +10
Computer Science · Decision Sciences · Mathematics · #Big Data Technologies and Applications #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Privacy-Preserving Technologies in Data #cs.CR #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1911.03674
5 pages, 3 figures, 2 tables, submitted to icassp 2019
arxiv created 2019/11/09 · openalex publication_date 2019/11/09 · arxiv updated 2019/11/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.