  In this paper, we present an approach based on reinforcement learning for eye
tracking data manipulation. It is based on two opposing agents, where one tries
to classify the data correctly and the second agent looks for patterns in the
data, which get manipulated to hide specific information. We show that our
approach is successfully applicable to preserve the privacy of the subjects.
For this purpose, we evaluate our approach iteratively to showcase the behavior
of the reinforcement learning based approach. In addition, we evaluate the
importance of temporal, as well as spatial, information of eye tracking data
for specific classification goals. In the last part of our evaluation, we apply
the procedure to further public data sets without re-training the autoencoder
or the data manipulator. The results show that the learned manipulation is
generalized and applicable to unseen data as well.
