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Register a new userPreservation of Anomalous Subgroups On Machine Learning Transformed Data
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Kommy Weldemariam Dr
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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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 groups 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.
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Machine Learning services are being deployed in a large range of applications that make it easy for an adversary, using the algorithm and/or the model, to gain access to sensitive data. This paper investigates fundamental bounds on information leakage. First, we identify and bound the success rate of the worst-case membership inference attack, connecting it to the generalization error of the target model. Second, we study the question of how much sensitive information is stored by the algorithm about the training set and we derive bounds on the mutual information between the sensitive attributes and model parameters. Although our contributions are mostly of theoretical nature, the bounds and involved concepts are of practical relevance. Inspired by our theoretical analysis, we study linear regression and DNN models to illustrate how these bounds can be used to assess the privacy guarantees of ML models.
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