Although machine learning and especially deep learning methods have played an
important role in the field of information management, privacy protection is an
important and concerning topic in current machine learning models. In
information management field, a large number of texts containing personal
information are produced by users every day. As the model training on
information from users is likely to invade personal privacy, many methods have
been proposed to block the learning and memorizing of the sensitive data in raw
texts. In this paper, we try to do this more linguistically via distorting the
text while preserving the semantics. In practice, we leverage a recently our
proposed metric, Neighboring Distribution Divergence, to evaluate the semantic
preservation during the distortion. Based on the metric, we propose two
frameworks for semantics-preserved distortion, a generative one and a
substitutive one. We conduct experiments on named entity recognition,
constituency parsing, and machine reading comprehension tasks. Results from our
experiments show the plausibility and efficiency of our distortion as a method
for personal privacy protection. Moreover, we also evaluate the attribute
attack on three privacy-related tasks in the current natural language
processing field, and the results show the simplicity and effectiveness of our
data-based improvement approach compared to the structural improvement
approach. Further, we also investigate the effects of privacy protection in
specific medical information management in this work and show that the medical
information pre-training model using our approach can effectively reduce the
memory of patients and symptoms, which fully demonstrates the practicality of
our approach.

本研究提出了一种以语义保持的方式扭曲文本以达到保护个人隐私的方法，实验表明该方法在数据隐私保护以及特定医疗信息管理上有着实际意义.