Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning

TL;DR

This paper presents DP-auto-GAN, a framework combining autoencoders and GANs for differentially private synthetic data generation from mixed-type sensitive data, ensuring privacy and statistical similarity.

Contribution

The paper introduces DP-auto-GAN, a novel framework for private synthetic data generation applicable to mixed data types, with a new metric for diversity assessment.

Findings

Effective generation of synthetic data for binary and mixed types

Comparable or improved performance over existing private algorithms

A new metric for diversity detection in synthetic data

Abstract

We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversarial Networks (GANs). This framework can be used to take in raw sensitive data and privately train a model for generating synthetic data that will satisfy similar statistical properties as the original data. This learned model can generate an arbitrary amount of synthetic data, which can then be freely shared due to the post-processing guarantee of differential privacy. Our framework is applicable to unlabeled mixed-type data, that may include binary, categorical, and real-valued data. We implement this framework on both binary data (MIMIC-III) and mixed-type data (ADULT), and compare its performance with existing private algorithms on metrics in unsupervised settings. We also introduce a new quantitative metric able to detect diversity, or lack thereof, of synthetic data.