Tabular Data Synthesis Using Generative Adversarial network

This project is about synthesizing data using generative adversarial network (GAN). Unlike conventional studies which use anonymization techniques for removing private information of individuals, we use variants of GAN architectures for crafting new records contextually like real records in the legitimate dataset. We plan to run exploratory experiments on public datasets to provide enough grounds for the viability of GANs in synthesizing information. The objective is to develop a proof of concept that shows if synthetic data could be used with similar results than original data.

Intern: 
Mohammad Esmaeilpour
Superviseur universitaire: 
Patrick Cardinal
Province: 
Quebec
Partner University: 
Discipline: