2 papers
cs.CR2024
Privacy Re-identification Attacks on Tabular GANs
Abdallah Alshantti, Adil Rasheed, Frank Westad
Generative models are subject to overfitting and thus may potentially leak sensitive information from the training data. In this work. we investigate the privacy risks that can pot…
cs.LG2023
CasTGAN: Cascaded Generative Adversarial Network for Realistic Tabular Data Synthesis
Abdallah Alshantti, Damiano Varagnolo, Adil Rasheed +2
Generative adversarial networks (GANs) have drawn considerable attention in recent years for their proven capability in generating synthetic data which can be utilised for multiple…