In recent years, machine learning - particularly deep learning - has significantly impacted the field of information management. While several strategies have been proposed to restrict models from learning and memorizing sensitive information from raw texts, this paper suggests a more linguistically-grounded approach to distort texts while maintaining semantic integrity. To this end, we leverage Neighboring Distribution Divergence, a novel metric to assess the preservation of semantic meaning during distortion. Building on this metric, we present two distinct frameworks for semantic-preserving distortion: a generative approach and a substitutive approach. Our evaluations across various tasks, including named entity recognition, constituency parsing, and machine reading comprehension, affirm the plausibility and efficacy of our distortion technique in personal privacy protection. We also test our method against attribute attacks in three privacy-focused assignments within the NLP domain, and the findings underscore the simplicity and efficacy of our data-based improvement approach over structural improvement approaches. Moreover, we explore privacy protection in a specific medical information management scenario, showing our method effectively limits sensitive data memorization, underscoring its practicality.

翻译：近年来，机器学习——尤其是深度学习——已对信息管理领域产生显著影响。尽管已有多种策略被提出以限制模型从原始文本中学习并记忆敏感信息，本文提出了一种更具语言学基础的方法，在保持语义完整性的同时对文本进行失真处理。为此，我们利用邻域分布散度这一新颖指标来评估失真过程中的语义保持程度。基于该指标，我们提出了两种不同的语义保持失真框架：生成式方法与替换式方法。我们在多项任务（包括命名实体识别、成分句法分析和机器阅读理解）上的评估结果证实了该失真技术在个人隐私保护方面的合理性与有效性。我们还在自然语言处理领域内三个注重隐私的任务中测试了该方法对属性攻击的防御效果，结果表明我们基于数据改进的方法相较于结构改进方法更为简洁高效。此外，我们在特定医疗信息管理场景中探索了隐私保护应用，证明该方法能有效限制敏感数据的记忆，突显了其实用价值。