Privacy-Preserving Fair Synthetic Tabular Data
This addresses the challenge of sharing private and biased tabular data for data publishers and users, though it is incremental as it builds on existing WGAN-GP methods.
The researchers tackled the problem of generating synthetic tabular data that preserves privacy and fairness while maintaining utility, presenting PF-WGAN which modifies WGAN-GP with privacy and fairness constraints. They found it achieves a more balanced trade-off among utility, privacy, and fairness compared to three state-of-the-art models across four datasets.
Sharing of tabular data containing valuable but private information is limited due to legal and ethical issues. Synthetic data could be an alternative solution to this sharing problem, as it is artificially generated by machine learning algorithms and tries to capture the underlying data distribution. However, machine learning models are not free from memorization and may introduce biases, as they rely on training data. Producing synthetic data that preserves privacy and fairness while maintaining utility close to the real data is a challenging task. This research simultaneously addresses both the privacy and fairness aspects of synthetic data, an area not explored by other studies. In this work, we present PF-WGAN, a privacy-preserving, fair synthetic tabular data generator based on the WGAN-GP model. We have modified the original WGAN-GP by adding privacy and fairness constraints forcing it to produce privacy-preserving fair data. This approach will enable the publication of datasets that protect individual's privacy and remain unbiased toward any particular group. We compared the results with three state-of-the-art synthetic data generator models in terms of utility, privacy, and fairness across four different datasets. We found that the proposed model exhibits a more balanced trade-off among utility, privacy, and fairness.