Relational Data Synthesis using Generative Adversarial Networks: A Design Space Exploration
This work addresses the need for effective privacy-preserving data publishing for database and machine learning practitioners, but it is incremental as it focuses on comparing existing methods under a unified framework.
The authors tackled the problem of privacy-preserving relational data synthesis by conducting a comprehensive experimental study using generative adversarial networks (GANs), finding that GANs are promising and providing guidance on design solutions, though they did not report specific numerical results.
The proliferation of big data has brought an urgent demand for privacy-preserving data publishing. Traditional solutions to this demand have limitations on effectively balancing the tradeoff between privacy and utility of the released data. Thus, the database community and machine learning community have recently studied a new problem of relational data synthesis using generative adversarial networks (GAN) and proposed various algorithms. However, these algorithms are not compared under the same framework and thus it is hard for practitioners to understand GAN's benefits and limitations. To bridge the gaps, we conduct so far the most comprehensive experimental study that investigates applying GAN to relational data synthesis. We introduce a unified GAN-based framework and define a space of design solutions for each component in the framework, including neural network architectures and training strategies. We conduct extensive experiments to explore the design space and compare with traditional data synthesis approaches. Through extensive experiments, we find that GAN is very promising for relational data synthesis, and provide guidance for selecting appropriate design solutions. We also point out limitations of GAN and identify future research directions.