  Federated Learning (FL) has emerged as a potentially powerful
privacy-preserving machine learning methodology, since it avoids exchanging
data between participants, but instead exchanges model parameters. FL has
traditionally been applied to image, voice and similar data, but recently it
has started to draw attention from domains including financial services where
the data is predominantly tabular. However, the work on tabular data has not
yet considered potential attacks, in particular attacks using Generative
Adversarial Networks (GANs), which have been successfully applied to FL for
non-tabular data. This paper is the first to explore leakage of private data in
Federated Learning systems that process tabular data. We design a Generative
Adversarial Networks (GANs)-based attack model which can be deployed on a
malicious client to reconstruct data and its properties from other
participants. As a side-effect of considering tabular data, we are able to
statistically assess the efficacy of the attack (without relying on human
observation such as done for FL for images). We implement our attack model in a
recently developed generic FL software framework for tabular data processing.
The experimental results demonstrate the effectiveness of the proposed attack
model, thus suggesting that further research is required to counter GAN-based
privacy attacks.
