论文标题
自然语言处理中的差异隐私：到目前为止的故事
Differential Privacy in Natural Language Processing: The Story So Far
论文作者
论文摘要
随着大数据的潮流继续影响自然语言处理（NLP）的景观，现代NLP方法的利用已在此数据中扎根，以解决各种基于文本的任务。毫无疑问，这些方法可以包括私人或个人身份的信息。因此，近年来，NLP中的隐私问题已经变得热情，这与新的增强隐私技术（PET）的发展相吻合。在这些宠物中，差异隐私在围绕数据隐私的对话中具有几种理想的品质。自然，问题是差异隐私是否适用于NLP的非结构化领域。这个主题引发了新的研究，该研究是一个基本目标统一的：如何将差异隐私适应NLP方法？本文旨在总结差异隐私，当前思维，尤其是必须考虑的至关重要的下一步。
As the tide of Big Data continues to influence the landscape of Natural Language Processing (NLP), the utilization of modern NLP methods has grounded itself in this data, in order to tackle a variety of text-based tasks. These methods without a doubt can include private or otherwise personally identifiable information. As such, the question of privacy in NLP has gained fervor in recent years, coinciding with the development of new Privacy-Enhancing Technologies (PETs). Among these PETs, Differential Privacy boasts several desirable qualities in the conversation surrounding data privacy. Naturally, the question becomes whether Differential Privacy is applicable in the largely unstructured realm of NLP. This topic has sparked novel research, which is unified in one basic goal: how can one adapt Differential Privacy to NLP methods? This paper aims to summarize the vulnerabilities addressed by Differential Privacy, the current thinking, and above all, the crucial next steps that must be considered.
