Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation. (arXiv:2205.01950v1 [cs.LG])

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 …

data lg privacy