Privacy-Preserving Publishing with Generative Adversarial Network (GAN) for Supporting Contact Tracing of Infectious Diseases

Generative artificial intelligence (AI) has become popular. The combination of increasingly complex datasets beyond human comprehension and the widespread availability of advanced computing systems—such as graphics processing unit (GPU) and tensor processing unit (TPU)—has driven the rapid advancement of generative AI. This technology has found applications in areas such as voice recognition, recommendation systems and data privacy preservation, which foster more data sharing and reuse. While challenges related to bias, fairness and uncertainty in AI continue to evolve, emerging government regulations aim to ensure ethical use and maximize societal benefits. In this paper, we present a system that leverages generative adversarial network (GAN) to enable privacy-preserving data publishing. The system supports contact tracing for infectious diseases like coronavirus disease 2019 (COVID-19) and monkey-pox. Evaluation using COVID-19 data highlights the practicality and effectiveness of our system.