论文标题
强化学习保存和操纵眼睛跟踪数据的学习
Reinforcement learning for the privacy preservation and manipulation of eye tracking data
论文作者
论文摘要
在本文中，我们提出了一种基于强化学习的方法，用于进行眼睛跟踪数据操纵。它基于两个相对的代理，其中一个试图正确对数据进行分类，而第二代理会在数据中寻找模式，这些模式被操纵以隐藏特定信息。我们表明，我们的方法成功地适用于保护对象的隐私。为此，我们迭代地评估了我们的方法，以展示基于强化学习的方法的行为。此外，我们评估了时间跟踪数据以进行特定分类目标的时间和空间信息的重要性。在评估的最后一部分中，我们将过程应用于更多公共数据集，而无需重新训练自动编码器或数据操作器。结果表明，学习的操作也广泛化，也适用于看不见的数据。
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.
