  We propose an adversarial learning framework that deals with the
privacy-utility tradeoff problem under two types of conditions: data-type
ignorant, and data-type aware. Under data-type aware conditions, the privacy
mechanism provides a one-hot encoding of categorical features, representing
exactly one class, while under data-type ignorant conditions the categorical
variables are represented by a collection of scores, one for each class. We use
a neural network architecture consisting of a generator and a discriminator,
where the generator consists of an encoder-decoder pair, and the discriminator
consists of an adversary and a utility provider. Unlike previous research
considering this kind of architecture, which leverages autoencoders (AEs)
without introducing any randomness, or variational autoencoders (VAEs) based on
learning latent representations which are then forced into a Gaussian
assumption, our proposed technique introduces randomness and removes the
Gaussian assumption restriction on the latent variables, only focusing on the
end-to-end stochastic mapping of the input to privatized data. We test our
framework on different datasets: MNIST, FashionMNIST, UCI Adult, and US Census
Demographic Data, providing a wide range of possible private and utility
attributes. We use multiple adversaries simultaneously to test our privacy
mechanism -- some trained from the ground truth data and some trained from the
perturbed data generated by our privacy mechanism. Through comparative
analysis, our results demonstrate better privacy and utility guarantees than
the existing works under similar, data-type ignorant conditions, even when the
latter are considered under their original restrictive single-adversary model.
