Reinforcement learning for the privacy preservation and manipulation of eye tracking data

Wolfgang Fuhl

Abstract:

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 itera-
tively 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 evalua-
tion, we apply the procedure to further public data sets with-
out re-training the autoencoder or the data manipulator. The
results show that the learned manipulation is generalized and
applicable to unseen data as well.