  Utility and privacy are two crucial measurements of the quality of synthetic
tabular data. While significant advancements have been made in privacy
measures, generating synthetic samples with high utility remains challenging.
To enhance the utility of synthetic samples, we propose a novel architecture
called the DownStream Feedback Generative Adversarial Network (DSF-GAN). This
approach incorporates feedback from a downstream prediction model during
training to augment the generator's loss function with valuable information.
Thus, DSF-GAN utilizes a downstream prediction task to enhance the utility of
synthetic samples. To evaluate our method, we tested it using two popular
datasets. Our experiments demonstrate improved model performance when training
on synthetic samples generated by DSF-GAN, compared to those generated by the
same GAN architecture without feedback. The evaluation was conducted on the
same validation set comprising real samples. All code and datasets used in this
research will be made openly available for ease of reproduction.
