Reinforcement learning for the privacy preservation and manipulation of eye tracking data
This addresses privacy concerns for individuals whose eye tracking data is collected, though it appears incremental as it applies existing RL techniques to a specific domain.
The paper tackles privacy preservation in eye tracking data by developing a reinforcement learning approach with two opposing agents: one classifies the data correctly while the other manipulates patterns to hide specific information, showing successful privacy protection and generalization to unseen data without retraining.
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.