HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generation
This work addresses the need for privacy-preserving synthetic data generation in machine learning, though it appears incremental as it builds on existing GAN methods with multi-agent extensions.
The paper tackles the problem of generating synthetic data that optimizes multiple criteria, such as realism and privacy preservation, by introducing HydraGAN, a multi-agent GAN approach. The result shows that HydraGAN outperforms baseline methods on three datasets in maximizing data realism, model accuracy, and minimizing re-identification risk.
Synthetic data generation overcomes limitations of real-world machine learning. Traditional methods are valuable for augmenting costly datasets but only optimize one criterion: realism. In this paper, we tackle the problem of generating synthetic data that optimize multiple criteria. This goal is necessary when real data are replaced by synthetic for privacy preservation. We introduce HydraGAN, a new approach to synthetic data generation that introduces multiple generator and discriminator agents into the system. The multi-agent GAN optimizes the goal of privacy-preservation as well as data realism. To facilitate multi-agent training, we adapt game-theoretic principles to offer equilibrium guarantees. We observe that HydraGAN outperforms baseline methods for three datasets for multiple criteria of maximizing data realism, maximizing model accuracy, and minimizing re-identification risk.