Generative Adversarial Networks for Real-Time Synthetic Data Generation in Privacy-Critical Applications

Abstract

Information security is one of the hottest themes in the age of digitalization. While biometrics provide an advanced level of control for access, it is also privacy-critical (Nishant, Kennedy, & Corbett, 2020). Developing and testing secure and privacy-respectful systems, however, requires a joint effort involving an extensive dataset, which is neither trivial to obtain nor trivial to distribute (Luthra & Mangla, 2018). Previous work in synthetic biology assures that artificially sustainable datasets for real-time needs seem to be realistic (Rajeev, Pati, Padhi, & Govindan, 2017). We believe that Generative Adversarial Networks (GANs) are strong candidate architectures for this purpose to obtain real-time biometric verification datasets. In this study, we propose an end-to-end modular and generic GAN architecture with customization techniques for this original research target. The suggested architecture is also modular with a software protocol tool called GANGas in an upstream remote service provider to build artificial air impairment datasets for smart city secure biometric verification evaluations (Kache & Seuring, 2017).