Privacy Risks of Tabular Generative Adversarial Networks: Re-identification Attacks and Reconstruction Attacks

Generative models, particularly tabular GANs, can pose major privacy risks by leaking sensitive information from the training data through memorization. Attackers can exploit this vulnerability to recover private training samples using re-identification attacks and reconstruction attacks.