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 potentially arise from the use of generative adversarial
networks (GANs) for creating tabular synthetic datasets. For the purpose, we
analyse the effects of re-identification attacks on synthetic data, i.e.,
attacks which aim at selecting samples that are predicted to correspond to
memorised training samples based on their proximity to the nearest synthetic
records. We thus consider multiple settings where different attackers might
have different access levels or knowledge of the generative model and
predictive, and assess which information is potentially most useful for
launching more successful re-identification attacks. In doing so we also
consider the situation for which re-identification attacks are formulated as
reconstruction attacks, i.e., the situation where an attacker uses evolutionary
multi-objective optimisation for perturbing synthetic samples closer to the
training space. The results indicate that attackers can indeed pose major
privacy risks by selecting synthetic samples that are likely representative of
memorised training samples. In addition, we notice that privacy threats
considerably increase when the attacker either has knowledge or has black-box
access to the generative models. We also find that reconstruction attacks
through multi-objective optimisation even increase the risk of identifying
confidential samples.