  Training a good deep learning model requires substantial data and computing
resources, which makes the resulting neural model a valuable intellectual
property. To prevent the neural network from being undesirably exploited,
non-transferable learning has been proposed to reduce the model generalization
ability in specific target domains. However, existing approaches require
labeled data for the target domain which can be difficult to obtain.
Furthermore, they do not have the mechanism to still recover the model's
ability to access the target domain. In this paper, we propose a novel
unsupervised non-transferable learning method for the text classification task
that does not require annotated target domain data. We further introduce a
secret key component in our approach for recovering the access to the target
domain, where we design both an explicit and an implicit method for doing so.
Extensive experiments demonstrate the effectiveness of our approach.
