  We present heuristics for solving the maximin problem induced by the
generative adversarial privacy setting for linear and convolutional neural
network (CNN) adversaries. In the linear adversary setting, we present a greedy
algorithm for approximating the optimal solution for the privatizer, which
performs better as the number of instances increases. We also provide an
analysis of the algorithm to show that it not only removes the features most
correlated with the private label first, but also preserves the prediction
accuracy of public labels that are sufficiently independent of the features
that are relevant to the private label. In the CNN adversary setting, we
present a method of hiding selected information from the adversary while
preserving the others through alternately optimizing the goals of the
privatizer and the adversary using neural network backpropagation. We
experimentally show that our method succeeds on a fixed adversary.
