Moving Conditional GAN Close to Data: Synthetic Tabular Data Generation and Its Experimental Evaluation

Recently, data has ousted oil as the most economical resource in the world, but most companies are reluctant to share customer/user data in pure form and on a large scale due to privacy concerns. Many innovative technologies (e.g., federated learning, split learning) are employed to meet the growing demand for privacy preservation. Despite these technologies, acquiring personal data in order to optimize utility, and then sharing it on a large scale, is still very challenging. Thanks to the rapid development of artificial intelligence (AI), a relatively new and promising solution to resolve these challenges is to generate synthetic data (SD) by mirroring the original dataset’s properties. SD is a promising solution to address growing privacy demands as well as the utility/analytics requirements of many industry stakeholders. In this paper, we propose and implement an SD generation method from a real dataset containing both numerical and categorical attributes by using an improved conditional generative adversarial network (CGAN), and we quantify the feasibility of SD on technical and theoretical grounds. We provide a detailed analysis of SD in original and anonymized forms with the help of multiple use cases, whereas prior research simply assumed that privacy issues in SD are small because AI models do not overfit or SD has a poor connection with real data. We provide insights into the characteristics of SD (distributions, value frequencies, correlations, etc.) produced by the CGAN in relation to the real data. To the best of our knowledge, this is the pioneering work that provides an experiment-based analysis of the quality, privacy, and utility of SD in relation to a real benchmark dataset.